A Review of Collaborative Adaptive Cruise Control for Vehicle Queuing Technology

With the development of the modern economy, the number of cars on the road continues to increase, leading to escalating problems with traffic congestion. This paper outlines the progression of autonomous driving technology, emphasizing that a single autonomous vehicle is incapable of effectively mitigating traffic congestion. To further enhance the intelligence of traffic systems, this paper explores the potential value and application of Cooperative Adaptive Cruise Control (CACC) within vehicle platooning technology, with an aim to alleviate road congestion and increase traffic efficiency. In terms of the scenarios and potential value involved, this paper highlights the positive impact of vehicle platooning technology on reducing aerodynamic drag, fuel consumption, carbon emissions, and enhancing road throughput. This technology can also improve road safety by reducing collision risks through real-time communication and coordination between vehicles. Moreover, by implementing vehicle platooning, road capacity can be increased, thereby alleviating traffic congestion. The paper also points out some technical difficulties and challenges associated with vehicle platooning technology, including communication reliability, sensor accuracy, automatic control algorithms, and safety assurance. A series of solutions are proposed to address the challenges faced by vehicle platooning technology. Furthermore, potential future trends in vehicle platooning technology are explored, such as experimental verification of larger scale vehicle platoons, and consideration of model uncertainty and interference robustness. In summary, this paper provides a comprehensive exploration of the potential and challenges of vehicle platooning technology in alleviating traffic congestion and enhancing traffic efficiency. By detailing the technical background, application scenarios, potential value, and solutions, this paper offers valuable guidance and research direction for the development of future intelligent traffic systems.