Survey of connected automated vehicle perception mode: from autonomy to interaction

Abstract

For enhancing the intelligence of urban traffic, connected automated vehicle (CAV) is recognised as the leading technology in the near future. With on-board sensors and communication devices, status of the vehicles can be obtained to better coordinate the traffic. However, the limited environmental perception range cannot lead to the best efficiency of the global urban traffic. In this study, a three-step evolution strategy of the CAV perception mode is proposed, from autonomous perception to interactive perception to networked perception. Key technologies in these three steps are studied. In autonomous perception, vehicle positioning and dynamic target tracking approaches are proposed. In interactive perception, a reliable multi-mode information exchange mechanism is studied. Finally, a new traffic big data storage and advanced analytics solution in networked perception is introduced. Related experiments about the above key issues are designed, implemented, and verified based on the hardware platform, open source dataset, and cloud platform, respectively. The results show that the positioning distance root mean square achieves 3.9 m, object tracking speed reaches 30fps, and communication average packet loss rate is 2%. As can be seen from testing and simulation results, our proposed approaches can meet technical requirements and support environment perception mode evolution of CAV.