From 61027c08267f4ee1b18395b241c1af1b6210de90 Mon Sep 17 00:00:00 2001 From: yuesli Date: Tue, 8 Oct 2024 09:56:15 +0200 Subject: [PATCH 01/12] add eval functions --- src/dg_commons/evals/evaluate_comfort.py | 57 +++++ src/dg_commons/evals/evaluate_efficiency.py | 123 +++++++++++ src/dg_commons/evals/evaluate_safety.py | 228 ++++++++++++++++++++ 3 files changed, 408 insertions(+) create mode 100644 src/dg_commons/evals/evaluate_comfort.py create mode 100644 src/dg_commons/evals/evaluate_efficiency.py create mode 100644 src/dg_commons/evals/evaluate_safety.py diff --git a/src/dg_commons/evals/evaluate_comfort.py b/src/dg_commons/evals/evaluate_comfort.py new file mode 100644 index 0000000..b5211d5 --- /dev/null +++ b/src/dg_commons/evals/evaluate_comfort.py @@ -0,0 +1,57 @@ +import numpy as np +from dg_commons import U, DgSampledSequence + + +def get_max_jerk(commands: DgSampledSequence[U]): + length = len(commands.timestamps) + last_command = commands.values[0] + last_t = commands.timestamps[0] + max_jerk = 0 + for idx in range(1, length): + cur_command = commands.values[idx] + cur_t = commands.timestamps[idx] + jerk = (cur_command.acc - last_command.acc) / float(cur_t - last_t) + if np.abs(jerk) > max_jerk: + max_jerk = np.abs(jerk) + last_command = cur_command + last_t = cur_t + return max_jerk + + +def get_acc_rms(commands: DgSampledSequence[U]): + """ + comfort measurement according to ISO 2631 + the rms of frequency weighted acceletation + the + :param states: + :return: + """ + acc_time = [] + for idx in range(len(commands.timestamps)): + acc_time.append(commands.values[idx].acc) + acc_time = np.array(acc_time) + n_acc = len(acc_time) + st = 0.1 + acc_freq = np.fft.rfft(acc_time) + freq = np.fft.rfftfreq(n=n_acc, d=st) + acc_freq_weighted = [] + for idx in range(len(freq)): + acc_freq_weighted.append(acc_freq[idx] * acc_freq_filter(freq[idx])) + acc_freq_weighted = np.array(acc_freq_weighted) + acc_time_weighted = np.fft.irfft(acc_freq_weighted) + acc_rms = 0 + for acc in acc_time_weighted: + acc_rms += np.square(acc) + acc_rms = np.sqrt(acc_rms/len(acc_time_weighted)) + return acc_rms + + +def acc_freq_filter(freq: float) -> float: + """ + reference: + Low order continuous-time filters for approximation of the ISO 2631-1 human vibration sensitivity weightings + :param freq: + :return: 3rd-order approximated weight for horizonal acceleration + """ + w = (14.55 * freq ** 2 + 6.026 * freq + 7.725) / (freq ** 3 + 15.02 * freq ** 2 + 51.63 * freq + 47.61) + return w diff --git a/src/dg_commons/evals/evaluate_efficiency.py b/src/dg_commons/evals/evaluate_efficiency.py new file mode 100644 index 0000000..a6fc431 --- /dev/null +++ b/src/dg_commons/evals/evaluate_efficiency.py @@ -0,0 +1,123 @@ +from math import sqrt +from typing import Optional + +import numpy as np +from commonroad.scenario.lanelet import Lanelet, LaneletNetwork +from dg_commons.geo import PoseState, SE2Transform +from dg_commons import X, U, DgSampledSequence, iterate_with_dt +from dg_commons.sim.goals import RefLaneGoal +from dg_commons.maps.lanes import DgLanelet +from dg_commons.seq.sequence import Timestamp + + +def distance_traveled(states: DgSampledSequence[X]) -> float: + dist: float = 0 + for it in iterate_with_dt(states): + dist += sqrt((it.v1.x - it.v0.x) ** 2 + (it.v1.y - it.v0.y) ** 2) + return dist + + +def actuation_effort(commands: DgSampledSequence[U]) -> float: + pass + + +def time_goal_lane_reached(lanelet_network: LaneletNetwork, goal_lane: RefLaneGoal, states: DgSampledSequence[X], + pos_tol: float = 0.8, + heading_tol: float = 0.08) -> Timestamp: + reached_time = -1.0 + # for idx in range(1, len(states.values) + 1): + # state = states.values[-idx] + # timestep = states.timestamps[-idx] + # reached = desired_lane_reached(lanelet_network, goal_lane, state, pos_tol, heading_tol) + # if not reached: + # break + # reached_time = timestep + for idx in range(0, len(states.values)): + state = states.values[idx] + timestep = states.timestamps[idx] + reached = desired_lane_reached(lanelet_network, goal_lane, state, pos_tol, heading_tol) + if reached: + reached_time = timestep + break + return float(reached_time) + + +def desired_lane_reached(lanelet_network: LaneletNetwork, goal_lane: RefLaneGoal, state: X, pos_tol: float, + heading_tol: float) -> bool: + """ + + :param state: the last ego state in the simulation + :param goal_lane: the desired lane + :return: True if the ego vehicle has reached the goal lane or any of its successors. Reached means the vehicle + center is close to the lane center and the heading is aligned with the lane. + """ + ego_posestate = PoseState(x=state.x, y=state.y, psi=state.psi) + ego_pose = SE2Transform.from_PoseState(ego_posestate) + ref_lane = goal_lane.ref_lane # dglanelet + lane_pose = ref_lane.lane_pose_from_SE2Transform(ego_pose) + while not lane_pose.along_inside: + if np.abs(lane_pose.along_lane) < 1.0 or lane_pose.along_lane - ref_lane.get_lane_length() < 1.0: + # not inside the lane but close enough + break + if lane_pose.along_after: + ref_lane = get_successor_dglane(lanelet_network, ref_lane) + if ref_lane is not None: + lane_pose = ref_lane.lane_pose_from_SE2Transform(ego_pose) + else: + break + if lane_pose.along_before: + ref_lane = get_predecessor_dglane(lanelet_network, ref_lane) + if ref_lane is not None: + lane_pose = ref_lane.lane_pose_from_SE2Transform(ego_pose) + else: + break + + if goal_lane.is_fulfilled(state): + return True + # vehicle still on the road and is inside the desired lane, check its pose + if lane_pose.lateral_inside and lane_pose.distance_from_center < pos_tol and abs( + lane_pose.relative_heading) < heading_tol: + return True + else: + return False + + +def get_lanelet_from_dglanelet(lanelet_network: LaneletNetwork, dglanelet: DgLanelet) -> Lanelet: + ref_point = dglanelet.control_points[1].q.p + lane_id = lanelet_network.find_lanelet_by_position([ref_point])[0][0] + lanelet = lanelet_network.find_lanelet_by_id(lane_id) + return lanelet + + +def get_successor_lane(lanelet_network: LaneletNetwork, cur_lane: Lanelet | DgLanelet) -> Optional[Lanelet]: + if isinstance(cur_lane, DgLanelet): + cur_lane = get_lanelet_from_dglanelet(lanelet_network, cur_lane) + if len(cur_lane.successor) > 0: + return lanelet_network.find_lanelet_by_id(cur_lane.successor[0]) + else: + return None + + +def get_successor_dglane(lanelet_network: LaneletNetwork, cur_lane: Lanelet | DgLanelet) -> Optional[DgLanelet]: + suc_lanelet = get_successor_lane(lanelet_network, cur_lane) + if suc_lanelet is None: + return None + else: + return DgLanelet.from_commonroad_lanelet(suc_lanelet) + + +def get_predecessor_lane(lanelet_network: LaneletNetwork, cur_lane: Lanelet | DgLanelet) -> Optional[Lanelet]: + if isinstance(cur_lane, DgLanelet): + cur_lane = get_lanelet_from_dglanelet(lanelet_network, cur_lane) + if len(cur_lane.predecessor) > 0: + return lanelet_network.find_lanelet_by_id(cur_lane.predecessor[0]) + else: + return None + + +def get_predecessor_dglane(lanelet_network: LaneletNetwork, cur_lane: Lanelet | DgLanelet) -> Optional[DgLanelet]: + pre_lanelet = get_predecessor_lane(lanelet_network, cur_lane) + if pre_lanelet is None: + return None + else: + return DgLanelet.from_commonroad_lanelet(pre_lanelet) diff --git a/src/dg_commons/evals/evaluate_safety.py b/src/dg_commons/evals/evaluate_safety.py new file mode 100644 index 0000000..aee6c18 --- /dev/null +++ b/src/dg_commons/evals/evaluate_safety.py @@ -0,0 +1,228 @@ +from typing import Mapping, MutableMapping +import numpy as np +from shapely import distance +from shapely.ops import nearest_points +from shapely.geometry import Polygon +from shapely.affinity import translate +from geometry import SE2_from_xytheta +from dg_commons import apply_SE2_to_shapely_geo +from dg_commons import PlayerName, X +from dg_commons.sim import CollisionReport +from dg_commons.sim.goals import TPlanningGoal +from dg_commons.sim.simulator import LogEntry +from dg_commons.sim.simulator_structures import SimLog, SimModel +from dg_commons.seq.sequence import Timestamp + + +def has_collision(cr_list: list[CollisionReport]) -> bool: + """Check whether the ego vehicle is involved in a collision.""" + ego_name = PlayerName("Ego") + collided_players: set[PlayerName] = set() + for cr in cr_list: + collided_players.update((cr.players.keys())) + has_collided = True if ego_name in collided_players else False + return has_collided + + +def get_min_dist(logs: SimLog, models: MutableMapping[PlayerName, SimModel], + missions: Mapping[PlayerName, TPlanningGoal], ego_name: PlayerName) -> ( + float, PlayerName, Timestamp): + """Get the minimum distance between the ego and any other agent throughout the simulation.""" + timesteps = logs[ego_name].states.timestamps + min_dist = np.inf + min_dist_agent = None + min_dist_t = None + for t in timesteps: + min_dist_at_t, min_dist_agent_at_t = get_min_dist_at_t(logs, models, missions, t, ego_name) + if min_dist_at_t < min_dist: + min_dist = min_dist_at_t + min_dist_agent = min_dist_agent_at_t + min_dist_t = t + return min_dist, min_dist_agent, min_dist_t + + +def get_min_ttc_max_drac(logs: SimLog, models: MutableMapping[PlayerName, SimModel], + missions: Mapping[PlayerName, TPlanningGoal], + ego_name: PlayerName) -> ( + float, PlayerName, Timestamp): + """Get te minimum ttc and maximum drac throughout the simulation. + Returns the value, the agent involved and the time when the value is obtained.""" + timesteps = logs[ego_name].states.timestamps + min_ttc = np.inf + min_ttc_agent = None + min_ttc_t = None + max_drac = -np.inf + max_drac_agent = None + max_drac_t = None + for t in timesteps: + ttc_at_t, ttc_agent_at_t, drac_at_t, drac_agent_at_t = get_ttc_drac_at_t(logs, models, missions, t, ego_name) + if ttc_at_t < min_ttc: + min_ttc = ttc_at_t + min_ttc_agent = ttc_agent_at_t + min_ttc_t = t + if drac_at_t > max_drac: + max_drac = drac_at_t + max_drac_agent = drac_agent_at_t + max_drac_t = t + return min_ttc, min_ttc_agent, min_ttc_t, max_drac, max_drac_agent, max_drac_t + + +def get_min_dist_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel], + missions: Mapping[PlayerName, TPlanningGoal], t: Timestamp, + ego_name: PlayerName) -> (float, PlayerName): + """ + Compute the minimum distance between the ego vehicle and the agents at current timestep. + :param logs: simulation log + :param models: models of all agents + :param missions: missions of all agents + :param t: sim time + :param ego_name: + :return: min dist, the closest agent from ego + """ + logs_at_t = logs.at_interp(t) + logs_at_t = _remove_finished_players(logs_at_t, missions) + if ego_name not in logs_at_t.keys(): + # ego accomplished its goal + return np.inf, None + ego_state = logs_at_t[ego_name].state + ego_model = models[ego_name] + ego_pos = np.array([ego_state.x, ego_state.y]) + min_dist = np.inf + min_dist_agent = None + for name, log in logs_at_t.items(): + if name == ego_name: + continue + # agent_pos = np.array([log.state.x, log.state.y]) + # dist = np.linalg.norm(agent_pos - ego_pos) + agent_state = log.state + agent_model = models[name] + dist, _ = _get_dist(ego_state, agent_state, ego_model, agent_model) + if dist < min_dist: + min_dist = dist + min_dist_agent = name + return min_dist, min_dist_agent + + +def get_ttc_drac_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel], + missions: Mapping[PlayerName, TPlanningGoal], t: Timestamp, ego_name: PlayerName) -> ( + float, PlayerName, float, PlayerName): + """ + Compute the minimum time-to-collision and the maximum deceleration-rate-to-avoid-collision for the ego vehicle + against all agents at current timestep. + :param logs: simulation log + :param models: models of all agents + :param missions: missions of all agents + :param t: sim time + :param ego_name: + :return: ttc, the opponent agent involved in ttc, drac, the opponent agent involved in drac + """ + # time to collision + logs_at_t = logs.at_interp(t) + logs_at_t = _remove_finished_players(logs_at_t, missions) + if ego_name not in logs_at_t.keys(): + # ego accomplished its goal + return np.inf, None, -np.inf, None + ego_state = logs_at_t[ego_name].state + ego_model = models[ego_name] + ego_pos = np.array([ego_state.x, ego_state.y]) + ego_vel = ego_state.vx * np.array([np.cos(ego_state.psi), np.sin(ego_state.psi)]) + min_time = np.inf + min_ttc_agent = None + max_drac = -np.inf + max_drac_agent = None + for name, log in logs_at_t.items(): + if name == ego_name: + continue + agent_state = log.state + agent_pos = np.array([agent_state.x, agent_state.y]) + agent_model = models[name] + agent_vel = agent_state.vx * np.array([np.cos(agent_state.psi), np.sin(agent_state.psi)]) + dist_center = np.linalg.norm(agent_pos - ego_pos) + rel_vel_along_dist = np.dot(agent_vel - ego_vel, (agent_pos - ego_pos) / dist_center) + if rel_vel_along_dist > 0 or np.abs(dist_center / rel_vel_along_dist) > 5.0: + # two agents are leaving each other or far enough + continue + ttc, ego_dtc, agent_dtc = _get_ttc(ego_state, agent_state, ego_model, agent_model) + drac = ego_state.vx ** 2 / (2 * ego_dtc) + if ttc < min_time: + min_time = ttc + min_ttc_agent = name + if drac > max_drac: + max_drac = drac + max_drac_agent = name + + return min_time, min_ttc_agent, max_drac, max_drac_agent + + +def _get_dist(state1: X, state2: X, model1: SimModel, model2: SimModel) -> (float, np.ndarray): + """get the minimum distance between two vehicles, considering their geometry""" + pose1 = SE2_from_xytheta([state1.x, state1.y, state1.psi]) + poly1 = apply_SE2_to_shapely_geo(model1.vg.outline_as_polygon, pose1) + pose2 = SE2_from_xytheta([state2.x, state2.y, state2.psi]) + poly2 = apply_SE2_to_shapely_geo(model2.vg.outline_as_polygon, pose2) + nearest_pts = nearest_points(poly1, poly2) + dist = distance(nearest_pts[0], nearest_pts[1]) + pt1 = np.array([nearest_pts[0].x, nearest_pts[0].y]) + pt2 = np.array([nearest_pts[1].x, nearest_pts[1].y]) + dir = (pt2 - pt1) / np.linalg.norm(pt2 - pt1) + return dist, dir + + +def _get_ttc(state1: X, state2: X, model1: SimModel, model2: SimModel) -> (float, float, float): + """ + Compute the time-to-collision and distance-to-collisions of the two vehicles, considering their geometry. + :param poly1: geometry of the first agent + :param poly2: geometry of the second agent + :param state1: current state of the first agent + :param state2: current state of the second agent + :return: time-to-collision, distance-to-collision for agent1. distance-to-collision for agent2 + """ + pose1 = SE2_from_xytheta([state1.x, state1.y, state1.psi]) + poly1 = apply_SE2_to_shapely_geo(model1.vg.outline_as_polygon, pose1) + pose2 = SE2_from_xytheta([state2.x, state2.y, state2.psi]) + poly2 = apply_SE2_to_shapely_geo(model2.vg.outline_as_polygon, pose2) + ttc, dist_to_collision1, dist_to_collision2 = _get_ttc_of_poly_and_state(poly1, poly2, state1, state2) + return ttc, dist_to_collision1, dist_to_collision2 + + +def _get_ttc_of_poly_and_state(poly1: Polygon, poly2: Polygon, state1: X, state2: X): + """ + Compute the time-to-collision and distance-to-collisions of the two polygons, considering their geometry. + :param poly1: geometry of the first agent + :param poly2: geometry of the second agent + :param state1: current state of the first agent + :param state2: current state of the second agent + :return: time-to-collision, distance-to-collision for agent1. distance-to-collision for agent2 + """ + time = 0 + max_time = 3.0 + timestep = 0.1 + v1 = state1.vx * np.array([np.cos(state1.psi), np.sin(state1.psi)]) + v2 = state2.vx * np.array([np.cos(state2.psi), np.sin(state2.psi)]) + delta_pos = (v2 - v1) * timestep + while not poly1.intersects(poly2): + if time > max_time: + break + # move poly2 + poly2 = translate(poly2, xoff=delta_pos[0], yoff=delta_pos[1]) + time += timestep + # if time <= max_time: + return time, state1.vx * time, state2.vx * time + # return np.inf, np.inf, np.inf + + +def _remove_finished_players(log_at_t: Mapping[PlayerName, LogEntry], missions: Mapping[PlayerName, TPlanningGoal]) -> \ + Mapping[PlayerName, LogEntry]: + """ + Remove the agents that have acomplished their goal. + :param log_at_t: collection of players and their log(state, command, etc.) at time t + :param missions: the goal of each player + :return: a now collection of running players and their logs. + """ + log_new = {} + for name, log in log_at_t.items(): + mission = missions[name] + state = log.state + if not mission.is_fulfilled(state): + log_new[name] = log + return log_new From e659bd4f46dbbf6444b18493bfdbad66d282cf43 Mon Sep 17 00:00:00 2001 From: yuesli Date: Tue, 8 Oct 2024 12:26:27 +0200 Subject: [PATCH 02/12] rename the files --- src/dg_commons/{evals/evaluate_comfort.py => eval/comfort.py} | 0 .../{evals/evaluate_efficiency.py => eval/efficiency.py} | 0 src/dg_commons/{evals/evaluate_safety.py => eval/safety.py} | 0 3 files changed, 0 insertions(+), 0 deletions(-) rename src/dg_commons/{evals/evaluate_comfort.py => eval/comfort.py} (100%) rename src/dg_commons/{evals/evaluate_efficiency.py => eval/efficiency.py} (100%) rename src/dg_commons/{evals/evaluate_safety.py => eval/safety.py} (100%) diff --git a/src/dg_commons/evals/evaluate_comfort.py b/src/dg_commons/eval/comfort.py similarity index 100% rename from src/dg_commons/evals/evaluate_comfort.py rename to src/dg_commons/eval/comfort.py diff --git a/src/dg_commons/evals/evaluate_efficiency.py b/src/dg_commons/eval/efficiency.py similarity index 100% rename from src/dg_commons/evals/evaluate_efficiency.py rename to src/dg_commons/eval/efficiency.py diff --git a/src/dg_commons/evals/evaluate_safety.py b/src/dg_commons/eval/safety.py similarity index 100% rename from src/dg_commons/evals/evaluate_safety.py rename to src/dg_commons/eval/safety.py From d51834f3d4b9871b40c809d68599e877e66826d8 Mon Sep 17 00:00:00 2001 From: yuesli Date: Wed, 23 Oct 2024 17:27:16 +0200 Subject: [PATCH 03/12] add unit test for safety eval --- src/dg_commons/eval/safety.py | 53 ++++++++------- .../test_eval/test_safety_eval.py | 67 +++++++++++++++++++ 2 files changed, 97 insertions(+), 23 deletions(-) create mode 100644 src/dg_commons_tests/test_eval/test_safety_eval.py diff --git a/src/dg_commons/eval/safety.py b/src/dg_commons/eval/safety.py index aee6c18..d770dd2 100644 --- a/src/dg_commons/eval/safety.py +++ b/src/dg_commons/eval/safety.py @@ -2,7 +2,7 @@ import numpy as np from shapely import distance from shapely.ops import nearest_points -from shapely.geometry import Polygon +from shapely.geometry import Polygon, Point from shapely.affinity import translate from geometry import SE2_from_xytheta from dg_commons import apply_SE2_to_shapely_geo @@ -25,14 +25,21 @@ def has_collision(cr_list: list[CollisionReport]) -> bool: def get_min_dist(logs: SimLog, models: MutableMapping[PlayerName, SimModel], - missions: Mapping[PlayerName, TPlanningGoal], ego_name: PlayerName) -> ( - float, PlayerName, Timestamp): - """Get the minimum distance between the ego and any other agent throughout the simulation.""" + missions: Mapping[PlayerName, TPlanningGoal], ego_name: PlayerName, + t_range: tuple[Timestamp|None, Timestamp|None] = (None, None)) -> tuple[float, PlayerName, Timestamp]: + """ + Get the minimum distance between the ego and any other agent throughout the simulation. + Only timesteps within t_range are considered. + """ timesteps = logs[ego_name].states.timestamps min_dist = np.inf min_dist_agent = None min_dist_t = None for t in timesteps: + if t_range[0] is not None and t < t_range[0]: + continue + if t_range[1] is not None and t > t_range[1]: + break min_dist_at_t, min_dist_agent_at_t = get_min_dist_at_t(logs, models, missions, t, ego_name) if min_dist_at_t < min_dist: min_dist = min_dist_at_t @@ -43,10 +50,12 @@ def get_min_dist(logs: SimLog, models: MutableMapping[PlayerName, SimModel], def get_min_ttc_max_drac(logs: SimLog, models: MutableMapping[PlayerName, SimModel], missions: Mapping[PlayerName, TPlanningGoal], - ego_name: PlayerName) -> ( - float, PlayerName, Timestamp): - """Get te minimum ttc and maximum drac throughout the simulation. - Returns the value, the agent involved and the time when the value is obtained.""" + ego_name: PlayerName, t_range: tuple[Timestamp|None, Timestamp|None] = (None, None)) -> tuple[float, PlayerName, Timestamp]: + """ + Get te minimum time-to-collision(ttc) and maximum deceleration-rate-to-avoid-collision(drac) throughout the + simulation. + Only timesteps within t_range are considered. + """ timesteps = logs[ego_name].states.timestamps min_ttc = np.inf min_ttc_agent = None @@ -55,6 +64,10 @@ def get_min_ttc_max_drac(logs: SimLog, models: MutableMapping[PlayerName, SimMod max_drac_agent = None max_drac_t = None for t in timesteps: + if t_range[0] is not None and t < t_range[0]: + continue + if t_range[1] is not None and t > t_range[1]: + break ttc_at_t, ttc_agent_at_t, drac_at_t, drac_agent_at_t = get_ttc_drac_at_t(logs, models, missions, t, ego_name) if ttc_at_t < min_ttc: min_ttc = ttc_at_t @@ -69,7 +82,7 @@ def get_min_ttc_max_drac(logs: SimLog, models: MutableMapping[PlayerName, SimMod def get_min_dist_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel], missions: Mapping[PlayerName, TPlanningGoal], t: Timestamp, - ego_name: PlayerName) -> (float, PlayerName): + ego_name: PlayerName) -> tuple[float, PlayerName]: """ Compute the minimum distance between the ego vehicle and the agents at current timestep. :param logs: simulation log @@ -86,14 +99,11 @@ def get_min_dist_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel] return np.inf, None ego_state = logs_at_t[ego_name].state ego_model = models[ego_name] - ego_pos = np.array([ego_state.x, ego_state.y]) min_dist = np.inf min_dist_agent = None for name, log in logs_at_t.items(): if name == ego_name: continue - # agent_pos = np.array([log.state.x, log.state.y]) - # dist = np.linalg.norm(agent_pos - ego_pos) agent_state = log.state agent_model = models[name] dist, _ = _get_dist(ego_state, agent_state, ego_model, agent_model) @@ -104,8 +114,7 @@ def get_min_dist_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel] def get_ttc_drac_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel], - missions: Mapping[PlayerName, TPlanningGoal], t: Timestamp, ego_name: PlayerName) -> ( - float, PlayerName, float, PlayerName): + missions: Mapping[PlayerName, TPlanningGoal], t: Timestamp, ego_name: PlayerName) -> tuple[float, PlayerName, float, PlayerName]: """ Compute the minimum time-to-collision and the maximum deceleration-rate-to-avoid-collision for the ego vehicle against all agents at current timestep. @@ -143,10 +152,11 @@ def get_ttc_drac_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel] # two agents are leaving each other or far enough continue ttc, ego_dtc, agent_dtc = _get_ttc(ego_state, agent_state, ego_model, agent_model) - drac = ego_state.vx ** 2 / (2 * ego_dtc) if ttc < min_time: min_time = ttc min_ttc_agent = name + + drac = ego_state.vx ** 2 / (2 * ego_dtc) if ego_dtc > 0 else np.inf if drac > max_drac: max_drac = drac max_drac_agent = name @@ -154,7 +164,7 @@ def get_ttc_drac_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel] return min_time, min_ttc_agent, max_drac, max_drac_agent -def _get_dist(state1: X, state2: X, model1: SimModel, model2: SimModel) -> (float, np.ndarray): +def _get_dist(state1: X, state2: X, model1: SimModel, model2: SimModel) -> tuple[float, tuple[Point, Point]]: """get the minimum distance between two vehicles, considering their geometry""" pose1 = SE2_from_xytheta([state1.x, state1.y, state1.psi]) poly1 = apply_SE2_to_shapely_geo(model1.vg.outline_as_polygon, pose1) @@ -164,11 +174,10 @@ def _get_dist(state1: X, state2: X, model1: SimModel, model2: SimModel) -> (floa dist = distance(nearest_pts[0], nearest_pts[1]) pt1 = np.array([nearest_pts[0].x, nearest_pts[0].y]) pt2 = np.array([nearest_pts[1].x, nearest_pts[1].y]) - dir = (pt2 - pt1) / np.linalg.norm(pt2 - pt1) - return dist, dir + return dist, nearest_pts -def _get_ttc(state1: X, state2: X, model1: SimModel, model2: SimModel) -> (float, float, float): +def _get_ttc(state1: X, state2: X, model1: SimModel, model2: SimModel) -> tuple[float, float, float]: """ Compute the time-to-collision and distance-to-collisions of the two vehicles, considering their geometry. :param poly1: geometry of the first agent @@ -185,7 +194,7 @@ def _get_ttc(state1: X, state2: X, model1: SimModel, model2: SimModel) -> (float return ttc, dist_to_collision1, dist_to_collision2 -def _get_ttc_of_poly_and_state(poly1: Polygon, poly2: Polygon, state1: X, state2: X): +def _get_ttc_of_poly_and_state(poly1: Polygon, poly2: Polygon, state1: X, state2: X) -> tuple[float, float, float]: """ Compute the time-to-collision and distance-to-collisions of the two polygons, considering their geometry. :param poly1: geometry of the first agent @@ -202,13 +211,11 @@ def _get_ttc_of_poly_and_state(poly1: Polygon, poly2: Polygon, state1: X, state2 delta_pos = (v2 - v1) * timestep while not poly1.intersects(poly2): if time > max_time: - break + return np.inf, np.inf, np.inf # move poly2 poly2 = translate(poly2, xoff=delta_pos[0], yoff=delta_pos[1]) time += timestep - # if time <= max_time: return time, state1.vx * time, state2.vx * time - # return np.inf, np.inf, np.inf def _remove_finished_players(log_at_t: Mapping[PlayerName, LogEntry], missions: Mapping[PlayerName, TPlanningGoal]) -> \ diff --git a/src/dg_commons_tests/test_eval/test_safety_eval.py b/src/dg_commons_tests/test_eval/test_safety_eval.py new file mode 100644 index 0000000..145ff2c --- /dev/null +++ b/src/dg_commons_tests/test_eval/test_safety_eval.py @@ -0,0 +1,67 @@ +import numpy as np +import matplotlib.pyplot as plt +import shapely.affinity +from geometry import SE2_from_xytheta +from shapely import distance +from shapely.ops import nearest_points +from dg_commons import apply_SE2_to_shapely_geo, X +from dg_commons.sim.models.vehicle_dynamic import VehicleStateDyn, VehicleModelDyn +from dg_commons.eval.safety import _get_ttc, _get_dist + + +def plot_polygon(polygon: shapely.geometry.Polygon, ax: plt.Axes, color='black', alpha=1.0): + x1, y1 = polygon.exterior.xy + ax.plot(x1, y1, color=color, alpha=alpha) + + +def plot_vehicle_at_t(poly: shapely.geometry.Polygon, state: X, t: float, ax: plt.Axes, color='black', alpha=1.0): + delta_x = state.vx * np.cos(state.psi) * t + delta_y = state.vx * np.sin(state.psi) * t + poly = shapely.affinity.translate(poly, xoff=delta_x, yoff=delta_y) + plot_polygon(poly, ax, color=color, alpha=alpha) + + +def test_safety_eval(): + # create shapely geometry from vehicle pose + # state1 = VehicleStateDyn(x=21.84, y=-2.2, psi=-0.72, vx=10.29, delta=0) + # state2 = VehicleStateDyn(x=32.11, y=-6.96, psi=-0.71, vx=3.3, delta=0) + state1 = VehicleStateDyn(x=0, y=0.0, psi=-0.72, vx=8.0, delta=0) + state2 = VehicleStateDyn(x=8.0, y=0.0, psi=-1.5, vx=3.3, delta=0) + model1 = VehicleModelDyn.default_car(state1) + model2 = VehicleModelDyn.default_car(state2) + pose1 = SE2_from_xytheta([state1.x, state1.y, state1.psi]) + poly1 = apply_SE2_to_shapely_geo(model1.vg.outline_as_polygon, pose1) + pose2 = SE2_from_xytheta([state2.x, state2.y, state2.psi]) + poly2 = apply_SE2_to_shapely_geo(model2.vg.outline_as_polygon, pose2) + # plot the vehicles + plt.figure() + ax = plt.gca() + plot_polygon(poly1, ax, color='b', alpha=0.5) + plot_polygon(poly2, ax, color='r', alpha=0.5) + ax.axis('equal') + + # compute the nearest points + dist, nearest_pts = _get_dist(state1, state2, model1, model2) + pt1 = np.array([nearest_pts[0].x, nearest_pts[0].y]) + pt2 = np.array([nearest_pts[1].x, nearest_pts[1].y]) + pts = np.stack([pt1, pt2]) + plt.scatter(pts[:, 0], pts[:, 1], color='red') + print('min dist between the vehicle is ', dist, "m") + + + # simulate the movement, stop when the two vehicles collide + ttc_sim, dtc1, dtc2 = _get_ttc(state1, state2, model1, model2) + print("the simulated time to collision is ", ttc_sim, "s") + print("dist to collision for vehicle1 is ", dtc1, "m") + print("dist to collision for vehicle2 is ", dtc2, "m") + # plot the vehicle poses when they collide(in case of no collision, plot the vehicle poses at t=3.0) + ttc_sim = ttc_sim if not np.isinf(ttc_sim) else 3.0 + plot_vehicle_at_t(poly1, state1, ttc_sim, ax, color='b') + plot_vehicle_at_t(poly2, state2, ttc_sim, ax, color='r') + + plt.show() + + +if __name__ == '__main__': + test_safety_eval() + \ No newline at end of file From 76e95cc1987dba224de242c6e660142a96b031fd Mon Sep 17 00:00:00 2001 From: yuesli Date: Wed, 23 Oct 2024 18:45:58 +0200 Subject: [PATCH 04/12] add unit test for efficiency metric --- src/dg_commons/eval/efficiency.py | 23 ++++------- .../test_eval/logs/collision_reports.pickle | 1 + .../test_eval/logs/lanelet_network.pickle | Bin 0 -> 73767 bytes .../test_eval/logs/log.pickle | Bin 0 -> 446908 bytes .../test_eval/logs/missions.pickle | Bin 0 -> 225151 bytes .../test_eval/logs/models.pickle | Bin 0 -> 30107 bytes src/dg_commons_tests/test_eval/logs/sim.mp4 | Bin 0 -> 488850 bytes .../test_eval/test_efficiency_eval.py | 38 ++++++++++++++++++ 8 files changed, 47 insertions(+), 15 deletions(-) create mode 100644 src/dg_commons_tests/test_eval/logs/collision_reports.pickle create mode 100644 src/dg_commons_tests/test_eval/logs/lanelet_network.pickle create mode 100644 src/dg_commons_tests/test_eval/logs/log.pickle create mode 100644 src/dg_commons_tests/test_eval/logs/missions.pickle create mode 100644 src/dg_commons_tests/test_eval/logs/models.pickle create mode 100644 src/dg_commons_tests/test_eval/logs/sim.mp4 create mode 100644 src/dg_commons_tests/test_eval/test_efficiency_eval.py diff --git a/src/dg_commons/eval/efficiency.py b/src/dg_commons/eval/efficiency.py index a6fc431..7bc935c 100644 --- a/src/dg_commons/eval/efficiency.py +++ b/src/dg_commons/eval/efficiency.py @@ -23,23 +23,14 @@ def actuation_effort(commands: DgSampledSequence[U]) -> float: def time_goal_lane_reached(lanelet_network: LaneletNetwork, goal_lane: RefLaneGoal, states: DgSampledSequence[X], pos_tol: float = 0.8, - heading_tol: float = 0.08) -> Timestamp: - reached_time = -1.0 - # for idx in range(1, len(states.values) + 1): - # state = states.values[-idx] - # timestep = states.timestamps[-idx] - # reached = desired_lane_reached(lanelet_network, goal_lane, state, pos_tol, heading_tol) - # if not reached: - # break - # reached_time = timestep - for idx in range(0, len(states.values)): - state = states.values[idx] - timestep = states.timestamps[idx] + heading_tol: float = 0.08) -> Timestamp|None: + reached_time = None + for idx, state in enumerate(states.values): reached = desired_lane_reached(lanelet_network, goal_lane, state, pos_tol, heading_tol) if reached: - reached_time = timestep + reached_time = states.timestamps[idx] break - return float(reached_time) + return reached_time def desired_lane_reached(lanelet_network: LaneletNetwork, goal_lane: RefLaneGoal, state: X, pos_tol: float, @@ -56,7 +47,7 @@ def desired_lane_reached(lanelet_network: LaneletNetwork, goal_lane: RefLaneGoal ref_lane = goal_lane.ref_lane # dglanelet lane_pose = ref_lane.lane_pose_from_SE2Transform(ego_pose) while not lane_pose.along_inside: - if np.abs(lane_pose.along_lane) < 1.0 or lane_pose.along_lane - ref_lane.get_lane_length() < 1.0: + if np.abs(lane_pose.along_lane) < 1.0 or np.abs(lane_pose.along_lane - ref_lane.get_lane_length()) < 1.0: # not inside the lane but close enough break if lane_pose.along_after: @@ -90,6 +81,7 @@ def get_lanelet_from_dglanelet(lanelet_network: LaneletNetwork, dglanelet: DgLan def get_successor_lane(lanelet_network: LaneletNetwork, cur_lane: Lanelet | DgLanelet) -> Optional[Lanelet]: + # note: only one successor is considered for now if isinstance(cur_lane, DgLanelet): cur_lane = get_lanelet_from_dglanelet(lanelet_network, cur_lane) if len(cur_lane.successor) > 0: @@ -107,6 +99,7 @@ def get_successor_dglane(lanelet_network: LaneletNetwork, cur_lane: Lanelet | Dg def get_predecessor_lane(lanelet_network: LaneletNetwork, cur_lane: Lanelet | DgLanelet) -> Optional[Lanelet]: + # note: only one predecessor is considered for now if isinstance(cur_lane, DgLanelet): cur_lane = get_lanelet_from_dglanelet(lanelet_network, cur_lane) if len(cur_lane.predecessor) > 0: diff --git 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+0200 Subject: [PATCH 05/12] minor --- src/dg_commons/eval/safety.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/dg_commons/eval/safety.py b/src/dg_commons/eval/safety.py index d770dd2..f8e121d 100644 --- a/src/dg_commons/eval/safety.py +++ b/src/dg_commons/eval/safety.py @@ -151,7 +151,7 @@ def get_ttc_drac_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel] if rel_vel_along_dist > 0 or np.abs(dist_center / rel_vel_along_dist) > 5.0: # two agents are leaving each other or far enough continue - ttc, ego_dtc, agent_dtc = _get_ttc(ego_state, agent_state, ego_model, agent_model) + ttc, ego_dtc, _ = _get_ttc(ego_state, agent_state, ego_model, agent_model) if ttc < min_time: min_time = ttc min_ttc_agent = name @@ -172,8 +172,6 @@ def _get_dist(state1: X, state2: X, model1: SimModel, model2: SimModel) -> tuple poly2 = apply_SE2_to_shapely_geo(model2.vg.outline_as_polygon, pose2) nearest_pts = nearest_points(poly1, poly2) dist = distance(nearest_pts[0], nearest_pts[1]) - pt1 = np.array([nearest_pts[0].x, nearest_pts[0].y]) - pt2 = np.array([nearest_pts[1].x, nearest_pts[1].y]) return dist, nearest_pts From d8a7ef0fefc5cc4f15799f5e5968a8bc1afb4646 Mon Sep 17 00:00:00 2001 From: yuesli Date: Wed, 23 Oct 2024 19:05:51 +0200 Subject: [PATCH 06/12] minor --- src/dg_commons_tests/test_eval/test_efficiency_eval.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/dg_commons_tests/test_eval/test_efficiency_eval.py b/src/dg_commons_tests/test_eval/test_efficiency_eval.py index c693a54..fbdfe3e 100644 --- a/src/dg_commons_tests/test_eval/test_efficiency_eval.py +++ b/src/dg_commons_tests/test_eval/test_efficiency_eval.py @@ -21,10 +21,7 @@ def test_efficiency_eval(): ego_goal_lane = missions[ego_name] ego_states = log[ego_name].states time_to_reach = time_goal_lane_reached(lanelet_network, ego_goal_lane, ego_states) - if time_to_reach < 0: - has_reached_the_goal = False - else: - has_reached_the_goal = True + has_reached_the_goal = time_to_reach is not None if has_reached_the_goal: print("Goal lane reached " + "at time " + str(time_to_reach)) else: From 13423e1fc96a3a1afd1e6ad2935a05575430453d Mon Sep 17 00:00:00 2001 From: yuesli Date: Wed, 23 Oct 2024 19:16:42 +0200 Subject: [PATCH 07/12] add unit test for comfort evaluation --- src/dg_commons/eval/comfort.py | 49 +++++++++---------- .../test_eval/test_comfort_eval.py | 21 ++++++++ 2 files changed, 43 insertions(+), 27 deletions(-) create mode 100644 src/dg_commons_tests/test_eval/test_comfort_eval.py diff --git a/src/dg_commons/eval/comfort.py b/src/dg_commons/eval/comfort.py index b5211d5..004ab03 100644 --- a/src/dg_commons/eval/comfort.py +++ b/src/dg_commons/eval/comfort.py @@ -1,42 +1,37 @@ import numpy as np -from dg_commons import U, DgSampledSequence +from dg_commons import U, DgSampledSequence, iterate_with_dt +from dg_commons.seq.sequence import Timestamp -def get_max_jerk(commands: DgSampledSequence[U]): - length = len(commands.timestamps) - last_command = commands.values[0] - last_t = commands.timestamps[0] +def get_max_jerk(commands: DgSampledSequence[U], t_range: tuple[Timestamp|None, Timestamp|None] = (None, None)): + """ + Get the maximum jerk of the planned action sequence. + Only timesteps within t_range are considered. + """ max_jerk = 0 - for idx in range(1, length): - cur_command = commands.values[idx] - cur_t = commands.timestamps[idx] - jerk = (cur_command.acc - last_command.acc) / float(cur_t - last_t) + for it in iterate_with_dt(commands): + if t_range[0] is not None and it.t1 < t_range[0]: + continue + if t_range[1] is not None and it.t1 > t_range[1]: + break + jerk = (it.v1.acc - it.v0.acc) / float(it.t1 - it.t0) if np.abs(jerk) > max_jerk: max_jerk = np.abs(jerk) - last_command = cur_command - last_t = cur_t return max_jerk -def get_acc_rms(commands: DgSampledSequence[U]): +def get_acc_rms(commands: DgSampledSequence[U], t_range: tuple[Timestamp|None, Timestamp|None] = (None, None)): """ - comfort measurement according to ISO 2631 - the rms of frequency weighted acceletation - the - :param states: - :return: + comfort measurement according to ISO 2631. returns the rms of frequency weighted acceletation + Only timesteps within t_range are considered. """ - acc_time = [] - for idx in range(len(commands.timestamps)): - acc_time.append(commands.values[idx].acc) - acc_time = np.array(acc_time) - n_acc = len(acc_time) + t_range_min = commands.timestamps[0] if t_range[0] is None else t_range[0] + t_range_max = commands.timestamps[-1] if t_range[1] is None else t_range[1] + acc_time = np.array([command.acc for command, timestep in zip(commands.values, commands.timestamps) if timestep >= t_range_min and timestep <= t_range_max]) st = 0.1 - acc_freq = np.fft.rfft(acc_time) - freq = np.fft.rfftfreq(n=n_acc, d=st) - acc_freq_weighted = [] - for idx in range(len(freq)): - acc_freq_weighted.append(acc_freq[idx] * acc_freq_filter(freq[idx])) + acc_freqs = np.fft.rfft(acc_time) + freqs = np.fft.rfftfreq(n=len(acc_time), d=st) + acc_freq_weighted = [acc_freq * acc_freq_filter(freq) for acc_freq, freq in zip(acc_freqs, freqs)] acc_freq_weighted = np.array(acc_freq_weighted) acc_time_weighted = np.fft.irfft(acc_freq_weighted) acc_rms = 0 diff --git a/src/dg_commons_tests/test_eval/test_comfort_eval.py b/src/dg_commons_tests/test_eval/test_comfort_eval.py new file mode 100644 index 0000000..e32b643 --- /dev/null +++ b/src/dg_commons_tests/test_eval/test_comfort_eval.py @@ -0,0 +1,21 @@ +import pickle +from dg_commons import PlayerName +from dg_commons.eval.comfort import get_max_jerk, get_acc_rms + + +def test_comfort_eval(): + file = open("src/dg_commons_tests/test_eval/logs/log.pickle", 'rb') + log = pickle.load(file) + file.close() + + ego_name = PlayerName("Ego") + ego_commands = log[ego_name].commands + max_jerk = get_max_jerk(ego_commands) + acc_rms = get_acc_rms(ego_commands) + print("Max jerk: " + str(max_jerk)) + print("Acc rms: " + str(acc_rms)) + + +if __name__ == '__main__': + test_comfort_eval() + \ No newline at end of file From b4b77c1b1459ffb4b2362e6598358e86134e3a6c Mon Sep 17 00:00:00 2001 From: yuesli Date: Wed, 23 Oct 2024 19:16:54 +0200 Subject: [PATCH 08/12] minor --- src/dg_commons/eval/safety.py | 5 ++--- src/dg_commons_tests/test_eval/test_efficiency_eval.py | 3 --- src/dg_commons_tests/test_eval/test_safety_eval.py | 2 -- 3 files changed, 2 insertions(+), 8 deletions(-) diff --git a/src/dg_commons/eval/safety.py b/src/dg_commons/eval/safety.py index f8e121d..4e580bb 100644 --- a/src/dg_commons/eval/safety.py +++ b/src/dg_commons/eval/safety.py @@ -28,7 +28,7 @@ def get_min_dist(logs: SimLog, models: MutableMapping[PlayerName, SimModel], missions: Mapping[PlayerName, TPlanningGoal], ego_name: PlayerName, t_range: tuple[Timestamp|None, Timestamp|None] = (None, None)) -> tuple[float, PlayerName, Timestamp]: """ - Get the minimum distance between the ego and any other agent throughout the simulation. + Get the minimum distance between the ego and any other agent. Only timesteps within t_range are considered. """ timesteps = logs[ego_name].states.timestamps @@ -52,8 +52,7 @@ def get_min_ttc_max_drac(logs: SimLog, models: MutableMapping[PlayerName, SimMod missions: Mapping[PlayerName, TPlanningGoal], ego_name: PlayerName, t_range: tuple[Timestamp|None, Timestamp|None] = (None, None)) -> tuple[float, PlayerName, Timestamp]: """ - Get te minimum time-to-collision(ttc) and maximum deceleration-rate-to-avoid-collision(drac) throughout the - simulation. + Get te minimum time-to-collision(ttc) and maximum deceleration-rate-to-avoid-collision(drac). Only timesteps within t_range are considered. """ timesteps = logs[ego_name].states.timestamps diff --git a/src/dg_commons_tests/test_eval/test_efficiency_eval.py b/src/dg_commons_tests/test_eval/test_efficiency_eval.py index fbdfe3e..e7dc6e5 100644 --- a/src/dg_commons_tests/test_eval/test_efficiency_eval.py +++ b/src/dg_commons_tests/test_eval/test_efficiency_eval.py @@ -1,7 +1,4 @@ import pickle -from pathlib import Path -import os -import numpy as np from dg_commons import PlayerName from dg_commons.eval.efficiency import time_goal_lane_reached, distance_traveled diff --git a/src/dg_commons_tests/test_eval/test_safety_eval.py b/src/dg_commons_tests/test_eval/test_safety_eval.py index 145ff2c..cff5eb8 100644 --- a/src/dg_commons_tests/test_eval/test_safety_eval.py +++ b/src/dg_commons_tests/test_eval/test_safety_eval.py @@ -2,8 +2,6 @@ import matplotlib.pyplot as plt import shapely.affinity from geometry import SE2_from_xytheta -from shapely import distance -from shapely.ops import nearest_points from dg_commons import apply_SE2_to_shapely_geo, X from dg_commons.sim.models.vehicle_dynamic import VehicleStateDyn, VehicleModelDyn from dg_commons.eval.safety import _get_ttc, _get_dist From c5f563e756f3bf5b1a24967b23f113251d06f6ad Mon Sep 17 00:00:00 2001 From: alezana Date: Sun, 27 Oct 2024 20:31:12 +0100 Subject: [PATCH 09/12] minors + fix for python3.9 --- makefiles/Pypi.mk | 2 + src/dg_commons/eval/__init__.py | 0 src/dg_commons/eval/comfort.py | 18 +++-- src/dg_commons/eval/efficiency.py | 29 ++++++--- src/dg_commons/eval/safety.py | 65 ++++++++++++------- src/dg_commons_tests/__init__.py | 4 +- src/dg_commons_tests/test_eval/__init__.py | 0 .../test_eval/test_comfort_eval.py | 7 +- .../test_eval/test_efficiency_eval.py | 25 ++++--- .../test_eval/test_safety_eval.py | 25 +++---- 10 files changed, 101 insertions(+), 74 deletions(-) create mode 100644 src/dg_commons/eval/__init__.py create mode 100644 src/dg_commons_tests/test_eval/__init__.py diff --git a/makefiles/Pypi.mk b/makefiles/Pypi.mk index 3e15c43..3730d88 100644 --- a/makefiles/Pypi.mk +++ b/makefiles/Pypi.mk @@ -6,9 +6,11 @@ version-publish: version: poetry version patch + build: rm -f dist/* rm -rf src/*.egg-info poetry build + publish: poetry publish diff --git a/src/dg_commons/eval/__init__.py b/src/dg_commons/eval/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/dg_commons/eval/comfort.py b/src/dg_commons/eval/comfort.py index 004ab03..65b91bb 100644 --- a/src/dg_commons/eval/comfort.py +++ b/src/dg_commons/eval/comfort.py @@ -1,9 +1,11 @@ +from __future__ import annotations + import numpy as np from dg_commons import U, DgSampledSequence, iterate_with_dt from dg_commons.seq.sequence import Timestamp -def get_max_jerk(commands: DgSampledSequence[U], t_range: tuple[Timestamp|None, Timestamp|None] = (None, None)): +def get_max_jerk(commands: DgSampledSequence[U], t_range: tuple[Timestamp | None, Timestamp | None] = (None, None)): """ Get the maximum jerk of the planned action sequence. Only timesteps within t_range are considered. @@ -20,14 +22,20 @@ def get_max_jerk(commands: DgSampledSequence[U], t_range: tuple[Timestamp|None, return max_jerk -def get_acc_rms(commands: DgSampledSequence[U], t_range: tuple[Timestamp|None, Timestamp|None] = (None, None)): +def get_acc_rms(commands: DgSampledSequence[U], t_range: tuple[Timestamp | None, Timestamp | None] = (None, None)): """ comfort measurement according to ISO 2631. returns the rms of frequency weighted acceletation Only timesteps within t_range are considered. """ t_range_min = commands.timestamps[0] if t_range[0] is None else t_range[0] t_range_max = commands.timestamps[-1] if t_range[1] is None else t_range[1] - acc_time = np.array([command.acc for command, timestep in zip(commands.values, commands.timestamps) if timestep >= t_range_min and timestep <= t_range_max]) + acc_time = np.array( + [ + command.acc + for command, timestep in zip(commands.values, commands.timestamps) + if timestep >= t_range_min and timestep <= t_range_max + ] + ) st = 0.1 acc_freqs = np.fft.rfft(acc_time) freqs = np.fft.rfftfreq(n=len(acc_time), d=st) @@ -37,7 +45,7 @@ def get_acc_rms(commands: DgSampledSequence[U], t_range: tuple[Timestamp|None, T acc_rms = 0 for acc in acc_time_weighted: acc_rms += np.square(acc) - acc_rms = np.sqrt(acc_rms/len(acc_time_weighted)) + acc_rms = np.sqrt(acc_rms / len(acc_time_weighted)) return acc_rms @@ -48,5 +56,5 @@ def acc_freq_filter(freq: float) -> float: :param freq: :return: 3rd-order approximated weight for horizonal acceleration """ - w = (14.55 * freq ** 2 + 6.026 * freq + 7.725) / (freq ** 3 + 15.02 * freq ** 2 + 51.63 * freq + 47.61) + w = (14.55 * freq**2 + 6.026 * freq + 7.725) / (freq**3 + 15.02 * freq**2 + 51.63 * freq + 47.61) return w diff --git a/src/dg_commons/eval/efficiency.py b/src/dg_commons/eval/efficiency.py index 7bc935c..a285578 100644 --- a/src/dg_commons/eval/efficiency.py +++ b/src/dg_commons/eval/efficiency.py @@ -1,13 +1,16 @@ +from __future__ import annotations + from math import sqrt from typing import Optional import numpy as np from commonroad.scenario.lanelet import Lanelet, LaneletNetwork -from dg_commons.geo import PoseState, SE2Transform + from dg_commons import X, U, DgSampledSequence, iterate_with_dt -from dg_commons.sim.goals import RefLaneGoal +from dg_commons.geo import PoseState, SE2Transform from dg_commons.maps.lanes import DgLanelet from dg_commons.seq.sequence import Timestamp +from dg_commons.sim.goals import RefLaneGoal def distance_traveled(states: DgSampledSequence[X]) -> float: @@ -21,9 +24,13 @@ def actuation_effort(commands: DgSampledSequence[U]) -> float: pass -def time_goal_lane_reached(lanelet_network: LaneletNetwork, goal_lane: RefLaneGoal, states: DgSampledSequence[X], - pos_tol: float = 0.8, - heading_tol: float = 0.08) -> Timestamp|None: +def time_goal_lane_reached( + lanelet_network: LaneletNetwork, + goal_lane: RefLaneGoal, + states: DgSampledSequence[X], + pos_tol: float = 0.8, + heading_tol: float = 0.08, +) -> Timestamp | None: reached_time = None for idx, state in enumerate(states.values): reached = desired_lane_reached(lanelet_network, goal_lane, state, pos_tol, heading_tol) @@ -33,8 +40,9 @@ def time_goal_lane_reached(lanelet_network: LaneletNetwork, goal_lane: RefLaneGo return reached_time -def desired_lane_reached(lanelet_network: LaneletNetwork, goal_lane: RefLaneGoal, state: X, pos_tol: float, - heading_tol: float) -> bool: +def desired_lane_reached( + lanelet_network: LaneletNetwork, goal_lane: RefLaneGoal, state: X, pos_tol: float, heading_tol: float +) -> bool: """ :param state: the last ego state in the simulation @@ -66,8 +74,11 @@ def desired_lane_reached(lanelet_network: LaneletNetwork, goal_lane: RefLaneGoal if goal_lane.is_fulfilled(state): return True # vehicle still on the road and is inside the desired lane, check its pose - if lane_pose.lateral_inside and lane_pose.distance_from_center < pos_tol and abs( - lane_pose.relative_heading) < heading_tol: + if ( + lane_pose.lateral_inside + and lane_pose.distance_from_center < pos_tol + and abs(lane_pose.relative_heading) < heading_tol + ): return True else: return False diff --git a/src/dg_commons/eval/safety.py b/src/dg_commons/eval/safety.py index 4e580bb..b5c9ec8 100644 --- a/src/dg_commons/eval/safety.py +++ b/src/dg_commons/eval/safety.py @@ -1,17 +1,20 @@ -from typing import Mapping, MutableMapping +from __future__ import annotations + +from typing import Mapping, MutableMapping, Optional + import numpy as np +from geometry import SE2_from_xytheta from shapely import distance -from shapely.ops import nearest_points -from shapely.geometry import Polygon, Point from shapely.affinity import translate -from geometry import SE2_from_xytheta -from dg_commons import apply_SE2_to_shapely_geo -from dg_commons import PlayerName, X +from shapely.geometry import Polygon, Point +from shapely.ops import nearest_points + +from dg_commons import apply_SE2_to_shapely_geo, PlayerName, X +from dg_commons.seq.sequence import Timestamp from dg_commons.sim import CollisionReport from dg_commons.sim.goals import TPlanningGoal from dg_commons.sim.simulator import LogEntry from dg_commons.sim.simulator_structures import SimLog, SimModel -from dg_commons.seq.sequence import Timestamp def has_collision(cr_list: list[CollisionReport]) -> bool: @@ -24,9 +27,13 @@ def has_collision(cr_list: list[CollisionReport]) -> bool: return has_collided -def get_min_dist(logs: SimLog, models: MutableMapping[PlayerName, SimModel], - missions: Mapping[PlayerName, TPlanningGoal], ego_name: PlayerName, - t_range: tuple[Timestamp|None, Timestamp|None] = (None, None)) -> tuple[float, PlayerName, Timestamp]: +def get_min_dist( + logs: SimLog, + models: MutableMapping[PlayerName, SimModel], + missions: Mapping[PlayerName, TPlanningGoal], + ego_name: PlayerName, + t_range: tuple[Timestamp | None, Timestamp | None] = (None, None), +) -> tuple[float, PlayerName, Timestamp]: """ Get the minimum distance between the ego and any other agent. Only timesteps within t_range are considered. @@ -48,9 +55,13 @@ def get_min_dist(logs: SimLog, models: MutableMapping[PlayerName, SimModel], return min_dist, min_dist_agent, min_dist_t -def get_min_ttc_max_drac(logs: SimLog, models: MutableMapping[PlayerName, SimModel], - missions: Mapping[PlayerName, TPlanningGoal], - ego_name: PlayerName, t_range: tuple[Timestamp|None, Timestamp|None] = (None, None)) -> tuple[float, PlayerName, Timestamp]: +def get_min_ttc_max_drac( + logs: SimLog, + models: MutableMapping[PlayerName, SimModel], + missions: Mapping[PlayerName, TPlanningGoal], + ego_name: PlayerName, + t_range: tuple[Timestamp | None, Timestamp | None] = (None, None), +) -> tuple[float, PlayerName, Timestamp]: """ Get te minimum time-to-collision(ttc) and maximum deceleration-rate-to-avoid-collision(drac). Only timesteps within t_range are considered. @@ -79,9 +90,13 @@ def get_min_ttc_max_drac(logs: SimLog, models: MutableMapping[PlayerName, SimMod return min_ttc, min_ttc_agent, min_ttc_t, max_drac, max_drac_agent, max_drac_t -def get_min_dist_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel], - missions: Mapping[PlayerName, TPlanningGoal], t: Timestamp, - ego_name: PlayerName) -> tuple[float, PlayerName]: +def get_min_dist_at_t( + logs: SimLog, + models: MutableMapping[PlayerName, SimModel], + missions: Mapping[PlayerName, TPlanningGoal], + t: Timestamp, + ego_name: PlayerName, +) -> tuple[float, Optional[PlayerName]]: """ Compute the minimum distance between the ego vehicle and the agents at current timestep. :param logs: simulation log @@ -112,8 +127,13 @@ def get_min_dist_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel] return min_dist, min_dist_agent -def get_ttc_drac_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel], - missions: Mapping[PlayerName, TPlanningGoal], t: Timestamp, ego_name: PlayerName) -> tuple[float, PlayerName, float, PlayerName]: +def get_ttc_drac_at_t( + logs: SimLog, + models: MutableMapping[PlayerName, SimModel], + missions: Mapping[PlayerName, TPlanningGoal], + t: Timestamp, + ego_name: PlayerName, +) -> tuple[float, Optional[PlayerName], float, Optional[PlayerName]]: """ Compute the minimum time-to-collision and the maximum deceleration-rate-to-avoid-collision for the ego vehicle against all agents at current timestep. @@ -155,7 +175,7 @@ def get_ttc_drac_at_t(logs: SimLog, models: MutableMapping[PlayerName, SimModel] min_time = ttc min_ttc_agent = name - drac = ego_state.vx ** 2 / (2 * ego_dtc) if ego_dtc > 0 else np.inf + drac = ego_state.vx**2 / (2 * ego_dtc) if ego_dtc > 0 else np.inf if drac > max_drac: max_drac = drac max_drac_agent = name @@ -215,10 +235,11 @@ def _get_ttc_of_poly_and_state(poly1: Polygon, poly2: Polygon, state1: X, state2 return time, state1.vx * time, state2.vx * time -def _remove_finished_players(log_at_t: Mapping[PlayerName, LogEntry], missions: Mapping[PlayerName, TPlanningGoal]) -> \ - Mapping[PlayerName, LogEntry]: +def _remove_finished_players( + log_at_t: Mapping[PlayerName, LogEntry], missions: Mapping[PlayerName, TPlanningGoal] +) -> Mapping[PlayerName, LogEntry]: """ - Remove the agents that have acomplished their goal. + Remove the agents that have accomplished their goal. :param log_at_t: collection of players and their log(state, command, etc.) at time t :param missions: the goal of each player :return: a now collection of running players and their logs. diff --git a/src/dg_commons_tests/__init__.py b/src/dg_commons_tests/__init__.py index 3c737e5..ce1b474 100644 --- a/src/dg_commons_tests/__init__.py +++ b/src/dg_commons_tests/__init__.py @@ -1,4 +1,4 @@ -# OUT_TESTS_DIR = "out/test-results" from pathlib import Path -OUT_TESTS_DIR = str(Path(__file__).parent.parent.parent / "out/test-results") +REPO_DIR: Path = Path(__file__).parent.parent.parent +OUT_TESTS_DIR: str = str(REPO_DIR / "out/test-results") diff --git a/src/dg_commons_tests/test_eval/__init__.py b/src/dg_commons_tests/test_eval/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/dg_commons_tests/test_eval/test_comfort_eval.py b/src/dg_commons_tests/test_eval/test_comfort_eval.py index e32b643..1841c27 100644 --- a/src/dg_commons_tests/test_eval/test_comfort_eval.py +++ b/src/dg_commons_tests/test_eval/test_comfort_eval.py @@ -4,7 +4,7 @@ def test_comfort_eval(): - file = open("src/dg_commons_tests/test_eval/logs/log.pickle", 'rb') + file = open("src/dg_commons_tests/test_eval/logs/log.pickle", "rb") log = pickle.load(file) file.close() @@ -14,8 +14,3 @@ def test_comfort_eval(): acc_rms = get_acc_rms(ego_commands) print("Max jerk: " + str(max_jerk)) print("Acc rms: " + str(acc_rms)) - - -if __name__ == '__main__': - test_comfort_eval() - \ No newline at end of file diff --git a/src/dg_commons_tests/test_eval/test_efficiency_eval.py b/src/dg_commons_tests/test_eval/test_efficiency_eval.py index e7dc6e5..2d7f485 100644 --- a/src/dg_commons_tests/test_eval/test_efficiency_eval.py +++ b/src/dg_commons_tests/test_eval/test_efficiency_eval.py @@ -1,18 +1,20 @@ import pickle + from dg_commons import PlayerName from dg_commons.eval.efficiency import time_goal_lane_reached, distance_traveled +from dg_commons_tests import REPO_DIR def test_efficiency_eval(): - file = open("src/dg_commons_tests/test_eval/logs/lanelet_network.pickle", 'rb') - lanelet_network = pickle.load(file) - file.close() - file = open("src/dg_commons_tests/test_eval/logs/log.pickle", 'rb') - log = pickle.load(file) - file.close() - file = open("src/dg_commons_tests/test_eval/logs/missions.pickle", 'rb') - missions = pickle.load(file) - file.close() + logs = REPO_DIR / "src/dg_commons_tests/test_eval/logs" + with open(logs / "lanelet_network.pickle", "rb") as file: + lanelet_network = pickle.load(file) + + with open(logs / "log.pickle", "rb") as file: + log = pickle.load(file) + + with open(logs / "missions.pickle", "rb") as file: + missions = pickle.load(file) ego_name = PlayerName("Ego") ego_goal_lane = missions[ego_name] @@ -25,8 +27,3 @@ def test_efficiency_eval(): print("Goal lane not reached.") dist = distance_traveled(ego_states) print("Distance traveled: " + str(dist)) - - -if __name__ == '__main__': - test_efficiency_eval() - \ No newline at end of file diff --git a/src/dg_commons_tests/test_eval/test_safety_eval.py b/src/dg_commons_tests/test_eval/test_safety_eval.py index cff5eb8..a8fb860 100644 --- a/src/dg_commons_tests/test_eval/test_safety_eval.py +++ b/src/dg_commons_tests/test_eval/test_safety_eval.py @@ -7,12 +7,12 @@ from dg_commons.eval.safety import _get_ttc, _get_dist -def plot_polygon(polygon: shapely.geometry.Polygon, ax: plt.Axes, color='black', alpha=1.0): +def plot_polygon(polygon: shapely.geometry.Polygon, ax: plt.Axes, color="black", alpha=1.0): x1, y1 = polygon.exterior.xy ax.plot(x1, y1, color=color, alpha=alpha) -def plot_vehicle_at_t(poly: shapely.geometry.Polygon, state: X, t: float, ax: plt.Axes, color='black', alpha=1.0): +def plot_vehicle_at_t(poly: shapely.geometry.Polygon, state: X, t: float, ax: plt.Axes, color="black", alpha=1.0): delta_x = state.vx * np.cos(state.psi) * t delta_y = state.vx * np.sin(state.psi) * t poly = shapely.affinity.translate(poly, xoff=delta_x, yoff=delta_y) @@ -34,18 +34,17 @@ def test_safety_eval(): # plot the vehicles plt.figure() ax = plt.gca() - plot_polygon(poly1, ax, color='b', alpha=0.5) - plot_polygon(poly2, ax, color='r', alpha=0.5) - ax.axis('equal') + plot_polygon(poly1, ax, color="b", alpha=0.5) + plot_polygon(poly2, ax, color="r", alpha=0.5) + ax.axis("equal") # compute the nearest points dist, nearest_pts = _get_dist(state1, state2, model1, model2) pt1 = np.array([nearest_pts[0].x, nearest_pts[0].y]) pt2 = np.array([nearest_pts[1].x, nearest_pts[1].y]) pts = np.stack([pt1, pt2]) - plt.scatter(pts[:, 0], pts[:, 1], color='red') - print('min dist between the vehicle is ', dist, "m") - + plt.scatter(pts[:, 0], pts[:, 1], color="red") + print("min dist between the vehicle is ", dist, "m") # simulate the movement, stop when the two vehicles collide ttc_sim, dtc1, dtc2 = _get_ttc(state1, state2, model1, model2) @@ -54,12 +53,6 @@ def test_safety_eval(): print("dist to collision for vehicle2 is ", dtc2, "m") # plot the vehicle poses when they collide(in case of no collision, plot the vehicle poses at t=3.0) ttc_sim = ttc_sim if not np.isinf(ttc_sim) else 3.0 - plot_vehicle_at_t(poly1, state1, ttc_sim, ax, color='b') - plot_vehicle_at_t(poly2, state2, ttc_sim, ax, color='r') - + plot_vehicle_at_t(poly1, state1, ttc_sim, ax, color="b") + plot_vehicle_at_t(poly2, state2, ttc_sim, ax, color="r") plt.show() - - -if __name__ == '__main__': - test_safety_eval() - \ No newline at end of file From 6787658987a054e0fd7d437f2ca8bb6e4cca61c7 Mon Sep 17 00:00:00 2001 From: yuesli Date: Mon, 28 Oct 2024 14:44:18 +0100 Subject: [PATCH 10/12] improve test coverage --- .../test_eval/test_safety_eval.py | 42 +++++++++++++++++-- 1 file changed, 39 insertions(+), 3 deletions(-) diff --git a/src/dg_commons_tests/test_eval/test_safety_eval.py b/src/dg_commons_tests/test_eval/test_safety_eval.py index a8fb860..a1ef170 100644 --- a/src/dg_commons_tests/test_eval/test_safety_eval.py +++ b/src/dg_commons_tests/test_eval/test_safety_eval.py @@ -1,10 +1,12 @@ +import pickle import numpy as np import matplotlib.pyplot as plt import shapely.affinity from geometry import SE2_from_xytheta -from dg_commons import apply_SE2_to_shapely_geo, X +from dg_commons import apply_SE2_to_shapely_geo, X, PlayerName from dg_commons.sim.models.vehicle_dynamic import VehicleStateDyn, VehicleModelDyn -from dg_commons.eval.safety import _get_ttc, _get_dist +from dg_commons.eval.safety import _get_ttc, _get_dist, get_min_dist, get_min_ttc_max_drac, has_collision +from dg_commons_tests import REPO_DIR def plot_polygon(polygon: shapely.geometry.Polygon, ax: plt.Axes, color="black", alpha=1.0): @@ -19,7 +21,7 @@ def plot_vehicle_at_t(poly: shapely.geometry.Polygon, state: X, t: float, ax: pl plot_polygon(poly, ax, color=color, alpha=alpha) -def test_safety_eval(): +def test_dist_ttc_drac(): # create shapely geometry from vehicle pose # state1 = VehicleStateDyn(x=21.84, y=-2.2, psi=-0.72, vx=10.29, delta=0) # state2 = VehicleStateDyn(x=32.11, y=-6.96, psi=-0.71, vx=3.3, delta=0) @@ -56,3 +58,37 @@ def test_safety_eval(): plot_vehicle_at_t(poly1, state1, ttc_sim, ax, color="b") plot_vehicle_at_t(poly2, state2, ttc_sim, ax, color="r") plt.show() + + +def test_safety_eval(): + logs = REPO_DIR / "src/dg_commons_tests/test_eval/logs" + file = open(logs / "collision_reports.pickle", 'rb') + collision_reports = pickle.load(file) + file.close() + file = open(logs / "log.pickle", 'rb') + log = pickle.load(file) + file.close() + file = open(logs / "missions.pickle", 'rb') + missions = pickle.load(file) + file.close() + file = open(logs / "models.pickle", 'rb') + models = pickle.load(file) + file.close() + + ego_name = PlayerName("Ego") + + t_range = (0.2, 3.0) + min_dist, min_dist_agent, min_dist_t = get_min_dist(log, models, missions, ego_name, t_range) + min_ttc, min_ttc_agent, min_ttc_t, max_drac, max_drac_agent, max_drac_t = get_min_ttc_max_drac(log, models, + missions, + ego_name, t_range) + print( + "Minimum distance is %.2f" % min_dist + "m from agent " + str(min_dist_agent) + " at time step %.2f" % float( + min_dist_t) + "s.") + if not np.isinf(min_ttc): + print("Minimum time-to-collision is %.2f" % min_ttc + "s from agent " + str( + min_ttc_agent) + " at time step %.2f " % float(min_ttc_t) + "s.") + print("Maximum decelleration rate to avoid collision is %.2f" % max_drac + "m/s2 from agent " + str( + max_drac_agent) + " at time step %.2f " % float(max_drac_t) + "s.") + has_collided = has_collision(collision_reports) + print("Has collided: ", has_collided) From d185471a8d6988c3c931c4b5054a5b104cd7f59b Mon Sep 17 00:00:00 2001 From: yuesli Date: Mon, 28 Oct 2024 14:56:40 +0100 Subject: [PATCH 11/12] use f-string --- .../test_eval/test_efficiency_eval.py | 4 ++-- .../test_eval/test_safety_eval.py | 22 +++++++++---------- 2 files changed, 12 insertions(+), 14 deletions(-) diff --git a/src/dg_commons_tests/test_eval/test_efficiency_eval.py b/src/dg_commons_tests/test_eval/test_efficiency_eval.py index 2d7f485..21f2954 100644 --- a/src/dg_commons_tests/test_eval/test_efficiency_eval.py +++ b/src/dg_commons_tests/test_eval/test_efficiency_eval.py @@ -22,8 +22,8 @@ def test_efficiency_eval(): time_to_reach = time_goal_lane_reached(lanelet_network, ego_goal_lane, ego_states) has_reached_the_goal = time_to_reach is not None if has_reached_the_goal: - print("Goal lane reached " + "at time " + str(time_to_reach)) + print(f"Goal lane reached at time {time_to_reach:.2f}s") else: print("Goal lane not reached.") dist = distance_traveled(ego_states) - print("Distance traveled: " + str(dist)) + print(f"Distance traveled: {dist:.2f}m") diff --git a/src/dg_commons_tests/test_eval/test_safety_eval.py b/src/dg_commons_tests/test_eval/test_safety_eval.py index a1ef170..3d99066 100644 --- a/src/dg_commons_tests/test_eval/test_safety_eval.py +++ b/src/dg_commons_tests/test_eval/test_safety_eval.py @@ -46,13 +46,13 @@ def test_dist_ttc_drac(): pt2 = np.array([nearest_pts[1].x, nearest_pts[1].y]) pts = np.stack([pt1, pt2]) plt.scatter(pts[:, 0], pts[:, 1], color="red") - print("min dist between the vehicle is ", dist, "m") + print(f"min dist between the vehicle is {dist:.2f}m") # simulate the movement, stop when the two vehicles collide ttc_sim, dtc1, dtc2 = _get_ttc(state1, state2, model1, model2) - print("the simulated time to collision is ", ttc_sim, "s") - print("dist to collision for vehicle1 is ", dtc1, "m") - print("dist to collision for vehicle2 is ", dtc2, "m") + print(f"the simulated time to collision is {ttc_sim:.2f}s") + print(f"dist to collision for vehicle1 is {dtc1:.2f}m") + print(f"dist to collision for vehicle2 is {dtc2:.2f}m") # plot the vehicle poses when they collide(in case of no collision, plot the vehicle poses at t=3.0) ttc_sim = ttc_sim if not np.isinf(ttc_sim) else 3.0 plot_vehicle_at_t(poly1, state1, ttc_sim, ax, color="b") @@ -81,14 +81,12 @@ def test_safety_eval(): min_dist, min_dist_agent, min_dist_t = get_min_dist(log, models, missions, ego_name, t_range) min_ttc, min_ttc_agent, min_ttc_t, max_drac, max_drac_agent, max_drac_t = get_min_ttc_max_drac(log, models, missions, - ego_name, t_range) + ego_name, t_range) print( - "Minimum distance is %.2f" % min_dist + "m from agent " + str(min_dist_agent) + " at time step %.2f" % float( - min_dist_t) + "s.") + f"Minimum distance is {min_dist:.2f}m from agent {min_dist_agent} at time step {min_dist_t:.2f}s.") if not np.isinf(min_ttc): - print("Minimum time-to-collision is %.2f" % min_ttc + "s from agent " + str( - min_ttc_agent) + " at time step %.2f " % float(min_ttc_t) + "s.") - print("Maximum decelleration rate to avoid collision is %.2f" % max_drac + "m/s2 from agent " + str( - max_drac_agent) + " at time step %.2f " % float(max_drac_t) + "s.") + print(f"Minimum time-to-collision is {min_ttc:.2f}s from agent {min_ttc_agent} at time step {min_ttc_t:.2f}s.") + print(f"Maximum decelleration rate to avoid collision is {max_drac:.2f}m/s2 from agent {max_drac_agent} at time step {max_drac_t:.2f}s.") + has_collided = has_collision(collision_reports) - print("Has collided: ", has_collided) + print(f"Ego vehicle has collided: {has_collided}") From d2d05538d42d12fc93514e5816398d2fa0ae1502 Mon Sep 17 00:00:00 2001 From: yuesli Date: Tue, 29 Oct 2024 22:31:41 +0100 Subject: [PATCH 12/12] bugfix --- src/dg_commons/eval/efficiency.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/dg_commons/eval/efficiency.py b/src/dg_commons/eval/efficiency.py index a285578..5873502 100644 --- a/src/dg_commons/eval/efficiency.py +++ b/src/dg_commons/eval/efficiency.py @@ -30,12 +30,12 @@ def time_goal_lane_reached( states: DgSampledSequence[X], pos_tol: float = 0.8, heading_tol: float = 0.08, -) -> Timestamp | None: +) -> float | None: reached_time = None for idx, state in enumerate(states.values): reached = desired_lane_reached(lanelet_network, goal_lane, state, pos_tol, heading_tol) if reached: - reached_time = states.timestamps[idx] + reached_time = float(states.timestamps[idx]) break return reached_time

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