基于多源传感器与导航地图的端到端自动驾驶方法

作者：Zhu Bo; Zhang Jiwei; Tan Dongkui^*; Hu Xudong

来源：Journal of Automotive Safety and Energy, 2022, 13(4): 738-749.

DOI：10.3969/j.issn.1674-8484.2022.04.015

• autonomous vehicles
• camera
• end-to-end autonomous driving
• light-laser detection and ranging (LiDAR)
• navigation maps
• neural networks

This paper proposed a multiple-input single-output (end-to-end) autonomous driving decision control model based on multi-source sensors and navigation map to make up for the shortcomings of existing end-to-end autonomous driving methods in active obstacle avoidance and passing through intersection by using the PilotNet model based on Deep Neural Network (DNN). The sensor data in model’s input end consisted of three parts: a monocular front view camera, a 2-D top view obtained by 360° multi-layer Light-Laser Detection and Ranging (LiDAR), and a local navigation map based on accurate positioning. The model’s output end generated the steering wheel angle, which was a vehicle control command. Some multi-condition simulations and real vehicle tests were conducted. The results show that the root mean square error (RMSE) of steering wheel angle decreases by 37% compared with that by using the PilotNet model. Therefore, the model has the ability of active obstacle avoidance and intersection passing. ? Journal of Automotive Safety and Energy.

• 单位
  合肥工业大学