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Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System
Jing Chen1, Cong Zhao1, Shengchuan Jiang1
1Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China.
Summary
Autonomous vehicles (AVs) with intelligent speed control significantly enhance safety and comfort on rough roads. This deep reinforcement learning approach improves driving efficiency and reduces discomfort compared to traditional methods.
Area of Science:
- * Autonomous Driving Systems
- * Intelligent Transportation Systems
- * Deep Reinforcement Learning
Background:
- * Human drivers' imperfect control leads to accidents and discomfort on rough pavements.
- * Autonomous vehicles (AVs) and cooperative vehicle infrastructure systems (CVIS) promise improved road safety and efficiency.
- * Real-time data integration from sensors enables advanced decision-making for AVs.
Purpose of the Study:
- * To develop an intelligent speed control strategy for AVs within CVIS environments.
- * To enhance safety, driving efficiency, and ride comfort on rough road surfaces.
- * To leverage deep reinforcement learning (DRL) for optimal AV speed control.
Main Methods:
- * Representing rough pavement profiles using maximum comfortable speeds and vertical comfort evaluation.
- * Designing a DRL-based speed control model for car-following behavior.
- * Training and testing the DRL model in a stochastic environment using real-world car-following data and rough pavement information.
Main Results:
- * The DRL-based speed control model demonstrated significant improvements over model predictive control-based adaptive cruise control.
- * Achieved a 93.47% increase in computational efficiency.
- * Improved driving efficiency by 26.99%, longitudinal comfort by 58.33%, and vertical comfort by 6.05%.
Conclusions:
- * The proposed intelligent speed control approach for AVs is effective on rough pavements.
- * The DRL-based method shows excellent potential for practical application in enhancing AV performance.
- * This research contributes to safer, more efficient, and comfortable autonomous driving experiences.
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