Motional Ad LLC

Lubing Zhou

Jiong Yang

Xiaoli Meng

Karan Rajendra Shetti

Yilaun Guo

Scott D Pendleton

Guchan Ozbilgin

Yu Pan

Giancarlo Baldan

Shu-Kai Lin

Puneet Singhal

Xiaojun Sun

Laith Sahawneh

Ankit Dhall

In an example method, a perception system receives first data representing a point cloud having a plurality of points, and clusters the points into a plurality of clusters. The clusters include a first cluster representing a first portion of an object and a second cluster representing a second portion of the object. Further, the perception system generates a first bounding box enclosing at least the first cluster and the second cluster, and generates a second bounding box enclosing at least the first cluster and the second cluster. The perception system selects either the first bounding box or the second bounding box, and outputs second data representing the object. The second data includes an indication of the selected bounding box and an indication of the object.

LiDAR point cloud segmentation using box prediction

Provided are methods for track segment cleaning of tracked objects using neural networks, which can include detecting a first track segment and a second track segment. The method includes applying a machine learning model trained to determine if the first track segment and second track segment capture real objects and if the first track segment and the second track segment are representative of an identical object exterior to a vehicle. The method further includes combining the first track segment and the second track segment to form a single track segment having a single trajectory in response to the first track segment and the second track segment being determined to be representative of the identical object. Systems and computer program products are also provided.

Track segment cleaning of tracked objects

Provided are methods for object detection using radar and lidar fusion, which can include generating clusters combining clusters of point clouds for radar and lidar, respectively, from which fused features are determined using a deep learning model. Systems and computer program products are also provided.

Object detection using RADAR and LiDAR fusion

This disclosure describes the use of optical sensors to detect and characterize the state of traffic lights to assist with the navigation of autonomous vehicles. In particular, a specific optical configuration is shown that includes both a fixed-exposure sensor and an auto-exposure sensor. Imagery from the two sensor types can be combined to more accurately characterize the state of traffic signals at any particular intersection. Systems and methods for analyzing only select regions of the imagery captured by the traffic light detection system are also described.

Systems and methods for traffic light detection

Among other things, techniques are described for determining precedence order at a multiway stop. In embodiments, identifications are assigned to tracks, and young tracks are compared to stale tracks. A young track matches a stale track based on one or more factors. An identification of the young track is reassigned to an identification of the stale track, wherein the young track is determined to match the stale track based on the one or more factors. An earliest time of appearance of agents is determined based on identifications and in view of perception obscured areas. A precedence order for navigating through the intersection is determined based on local rules, the identifications, and the earliest time of appearance of agents, and the vehicle proceeds through the multiway stop intersection in accordance with the precedence order.

Precedence determination at multi-way stops

Provided are systems and methods for a deep learning based beam control. Sensor data associated with the environment and the corresponding detected objects from a perception system are obtained. Object features and image features are extracted. The extracted object features and image features are fused into fused features. A beam control status is predicted according to the fused features, wherein the beam control status indicates a high beam illumination intensity or a low beam illumination intensity of a light emitting device.

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Singapore, Singapore

Lubing Zhou