Research on Perception and Control System of Small Autonomous Driving Vehicles
DOI:
https://doi.org/10.71222/1qbwx336Keywords:
small autonomous vehicles, perception system, control system, path planning, sensor fusionAbstract
With the continuous advancement of autonomous driving technology, the application of small autonomous vehicles in changing environments has gradually attracted attention. This article discusses the core technologies of perception and control systems for small autonomous vehicles. An analysis was conducted on the key issues faced by the perception system of small autonomous vehicles, including improving the accuracy of local and global positioning, as well as the application of multi-source sensor fusion technology. By integrating various sensing devices such as LiDAR and image sensors, the perception accuracy of the system has been enhanced. The improvement of the control system was also discussed, and the overall path planning method based on gridded maps and the improvement strategy of the motion control system were analyzed. With precise path design and efficient motion control algorithms, the driving stability and safety factor of the car are ensured in changing environments. Finally, the integration and testing of perception and control systems were discussed, and solutions for software hardware collaboration enhancement and comprehensive debugging and testing in complex scenarios were proposed.
References
1. T. Sauer, M. Gorks, L. Spielmann, K. Zindler, and U. Jumar, "Adaptive self-learning controllers with disturbance compensation for automatic track guidance of industrial trucks," SICE J. Control Meas. Syst. Integr., vol. 16, no. 1, pp. 84–97, 2023, doi: 10.1080/18824889.2023.2183009.
2. S. Yahagi and M. Suzuki, "Intelligent PI control based on the ultra-local model and Kalman filter for vehicle yaw-rate control," SICE J. Control Meas. Syst. Integr., vol. 16, no. 1, pp. 38–47, 2023, doi: 10.1080/18824889.2023.2174648.
3. J. Chen et al., "Q-EANet: Implicit social modeling for trajectory prediction via experience-anchored queries," IET Intell. Transp. Syst., vol. 18, no. 6, pp. 1004–1015, 2024, doi: 10.1049/itr2.12477.
4. A. Astudillo, A. Barrera, C. Guindel et al., "DAttNet: monocular depth estimation network based on attention mechanisms," Neural Comput. Appl., vol. 36, pp. 3347–3356, 2024, doi: 10.1007/s00521-023-09210-8.
5. S. Zhang and T. Tak, "Risk analysis of autonomous vehicle test scenarios using a novel analytic hierarchy process method," IET Intell. Transp. Syst., vol. 18, no. 5, pp. 794–807, 2024, doi: 10.1049/itr2.12466.
6. F. Schandl, P. Fischer, and M. F. C. Hudecek, "Predicting acceptance of autonomous shuttle buses by personality profiles: a latent profile analysis," Transportation, vol. 52, pp. 1015–1038, 2025, doi: 10.1007/s11116-023-10447-4.
7. H. Li, H. Zhao, C. Li, Q. Wang, and X. Zhao, "Takeover behavior patterns for autonomous driving in crash scenarios," J. Transp. Saf. Secur., vol. 15, no. 11, pp. 1087–1115, 2022, doi: 10.1080/19439962.2022.2153954.