Controller initialization

This method places zero cost links in the topology graph representing availability of lane changes.

This function finds the shortest path connecting origin and destination using A* search with distance heuristic. origin

Estimate the throttle/brake of the vehicle based on the PID equations :param target_speed: target speed in Km/h :pa

Estimate the steering angle of the vehicle based on the PID equations :param waypoint: target waypoint :param vehicl

Refer to https://github.com/tianweiy/CenterPoint L1 regression loss Args: regr (batch x max_objects x dim) gt_regr (batch

Compute the type of connection between the current active waypoint and the multiple waypoints present in list_waypoints. The result is encode

This method returns the last successive intersection edge from a starting index on the route. This helps moving past tiny int

This method is in charge of tailgating behaviors. :param location: current location of the agent :param waypoint: cu

This method returns the turn decision (RoadOption) for pair of edges around current index of route list

This method updates the information regarding the ego vehicle based on the surrounding world.

Transforms coordinates of a vehicle bounding box to sensor.

Transforms coordinates of a vehicle bounding box to world.

Transforms world coordinates to sensor.

Overwrites the throttle a brake values of a control to perform an emergency stop. The steering is kept the same to avoid going out of

Add a small 3D axis on Open3D Visualizer

Aligns vector a to vector b with axis angle rotation

Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]

Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Ar

Args: gt_boxes: (N, 8) feature_map_size: (2), [x, y] Returns:

Args: input_dict: point_features: (N1 + N2 + N3 + ..., C) batch_size: point_coord

Args: input_dict: point_features: (N1 + N2 + N3 + ..., C) batch_size: point_coord

Args: input_dict: point_features: (N1 + N2 + N3 + ..., C) batch_size: point_coord

Args: anchors: (N, 7) [x, y, z, dx, dy, dz, heading] gt_boxes: (M, 7) [x, y, z, dx, dy, dz, heading] gt_c

Class-balanced sampling of nuScenes dataset from https://arxiv.org/abs/1908.09492

Args: im: (H, W, C) [y, x] x: (N) y: (N) Returns:

Args: boxes3d_fakelidar: (N, 7) [x, y, z, w, l, h, r] in old LiDAR coordinates, z is bottom center Returns: boxes3d_lidar: [

:param boxes3d: (N, 7) [x, y, z, l, h, w, ry] in camera coords, see the definition of ry in KITTI dataset :param bottom_center: whether y is

Args: boxes_a: (N, 4) [x1, y1, x2, y2] boxes_b: (M, 4) [x1, y1, x2, y2] Returns:

7 -------- 4 /| /| 6 -------- 5 . | | | | . 3 -------- 0 |/ |/ 2 -------- 1 A

Builds modules

Args: cls_scores: (N) iou_scores: (N) num_points_in_gt: (N, 7+c) cls_thresh: scalar

We assume that the 3D box has lower plane parallel to the ground. Returns: Polygon with 4 points describing the base.

fast iou algorithm. this function can be used independently to do result analysis. Must be used in CAMERA coordinate system. Args: gt_

fast iou algorithm. this function can be used independently to do result analysis. Must be used in CAMERA coordinate system. Args: gt_

fast iou algorithm. this function can be used independently to do result analysis. Must be used in CAMERA coordinate system. Args: gt_

Module in charge of car-following behaviors when there's someone in front of us. :param vehicle: car to follow

Convert a carla rotation to a roll, pitch, yaw tuple Considers the conversion from left-handed system (unreal) to right-handed system.

Args: x: x.features (N, C1) out_channels: C2 Returns: