LiDAR-based Object Detection Failure Tolerated Autonomous Driving Planning System

A typical autonomous driving system usually relies on the detected objects from an environment perception module. Current research still cannot guarantee a perfect perception, and failure detections may cause collisions, leading to untrustworthy autonomous vehicles. This work proposes a trajectory planner to tolerate the detection failure of the LiDAR sensors. This method will plan the path relying on the detected objects as well as the raw sensor data. The overlapping and contradiction of both perception routes will be carefully addressed for safe and efficient driving. The object detector in this work uses a deep learning-based method, i.e., CNN-Segmentation neural network. The designed trajectory planner has multi-layers to handle the multi-resolution environment formed by different perception routes. The final system will dynamically adjust its attention to the detected objects or the point cloud to avoid collision due to detection failures. This method is implemented on a real autonomous vehicle to drive in an open urban area. The results show that when the autonomous vehicle fails to detect a surrounding object, e.g., vehicles or some undefined objects, the autonomous vehicles still can plan an efficient and safe trajectory. In the meantime, when the perception system works well, the AV will not be affected by the point clouds. This technology can make the autonomous vehicle trustworthy even with the black-box neural networks. The codes are open-source with our autonomous driving platform to help other researchers for AV development.