Zhang, Zufeng (Department of Automation, Tsinghua University, Beijing,), Yin, Jialun (Tsinghua University), Lu, Weike (Soochow University), Zhang, Xuefeng (Institute for Artificial Intelligence, Peking University, Beijin), Tao, Qianwen (Wuhan University of Technology)

Enhancing Autonomous Driving through Collaborative Perception and Scene Situation Map Construction

Scheduled for presentation during the Regular Session "Sensing, Vision, and Perception V" (FrAT5), Friday, September 27, 2024, 11:50−12:10, Salon 13

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Abstract

The perception of autonomous driving is essential for vehicle localization and route planning. However, relying solely on vehicle sensors makes it challenging to achieve reliable and all-around perception. In this paper, we propose a method for constructing a scene situation map based on vehicle-road coordination. Firstly, we propose a panoramic camera and LiDAR fusion detection method located on the roadside to extract dynamic targets in the road environment. Then, on the vehicle side, we detect and track dynamic targets and associate them with the roadside by using dynamic target trajectory matching. Finally, we perform a graph optimization model between the vehicle, road, and HD map. By mapping the optimized dynamic targets onto a high-precision map, we generate a scene situation map. The effectiveness of the proposed method is validated through benchmark testing and a real-world field test.