| 16 | |
| 17 | |
| 18 | class UncontrolledPredictor(AbstractPredictor): |
| 19 | def predict( |
| 20 | self, observation: Observation, roadgraph: RoadGraph, |
| 21 | lastseen_vehicles, through_timestep, config) -> Prediction: |
| 22 | prediction = Prediction() |
| 23 | |
| 24 | for vehicle in observation.vehicles: |
| 25 | if vehicle.vtype != VehicleType.OUT_OF_AOI: |
| 26 | if vehicle.id in lastseen_vehicles: |
| 27 | prediction.results[vehicle] = lastseen_vehicles[vehicle.id].trajectory.states[through_timestep:] |
| 28 | else: |
| 29 | lane = roadgraph.get_lane_by_id(vehicle.lane_id) |
| 30 | predict_t = config["MIN_T"] |
| 31 | dt = config["DT"] |
| 32 | s = vehicle.current_state.s |
| 33 | d = vehicle.current_state.d |
| 34 | s_d = vehicle.current_state.s_d |
| 35 | |
| 36 | predict_trajectory = Trajectory() |
| 37 | for t in np.arange(0, predict_t, dt): |
| 38 | predict_trajectory.states.append( |
| 39 | State(t=t, d=d, s=s, s_d=s_d,)) |
| 40 | s += s_d * dt |
| 41 | next_lane = roadgraph.get_next_lane(lane.id) |
| 42 | lanes = [lane, next_lane] if next_lane != None else [lane] |
| 43 | predict_trajectory.frenet_to_cartesian( |
| 44 | lanes, vehicle.current_state) |
| 45 | |
| 46 | prediction.results[vehicle] = predict_trajectory.states |
| 47 | return prediction |