Deep-Learning-Based Intelligent Intervehicle Distance Control for 6G-Enabled Cooperative Autonomous Driving

Abstract

Research on the sixth-generation cellular networks (6G) is gaining huge momentum to achieve ubiquitous wireless connectivity. Connected autonomous vehicles (CAVs) is a critical vertical application for 6G, holding great potentials of improving road safety, road and energy efficiency. However, the stringent service requirements of CAV applications on reliability, latency, and high speed communications will present big challenges to 6G networks. New channel access algorithms and intelligent control schemes for connected vehicles are needed for 6G-supported CAV. In this article, we investigated 6G-supported cooperative driving, which is an advanced driving mode through information sharing and driving coordination. First, we quantify the delay upper bounds of 6G vehicle-to-vehicle (V2V) communications with hybrid communication and channel access technologies. A deep learning neural network is developed and trained for the fast computation of the delay bounds in real-time operations. Then, an intelligent strategy is designed to control the intervehicle distance for cooperative autonomous driving. Furthermore, we propose a Markov chain-based algorithm to predict the parameters of the system states, and also a safe distance mapping method to enable smooth vehicular speed changes. The proposed algorithms are implemented in the AirSim autonomous driving platform. Simulation results show that the proposed algorithms are effective and robust with safe and stable cooperative autonomous driving, which greatly improve the road safety, capacity, and efficiency.

Secure and Efficient Blockchain-Based Knowledge Sharing for Intelligent Connected Vehicles

“…proof: To prove the corollary, we first evaluate the monotonicity of the growth speed of CW in Eq. (9). By calculating the second derivation, it yields…”

“…Rula et.al proposed a data fusion algorithm to realize vehicular context awareness [8]. A joint rate control and resource allocation scheme was developed with the information of channel states and delay constraints [9]. During the information sharing process, it is crucial to guarantee the security and reliability, which has not been addressed in these works.…”

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

“…By incorporating advanced technologies, autonomous vehicles collaborate with each other directly over an intermediate infrastructure to improve the performance and efficiency of smart transportation systems if compared with individual autonomous vehicles without collaborative mechanisms. Indeed, the connected vehicle and automated vehicle technologies should be developed in parallel and closely cooperated to put forward completely smart transportation in the future [142]. To this end, besides 6G-enabled wireless communications, AI plays as one of the most important core technologies to process a massive amount of sensory data collected by multiple sensors, which helps autonomous vehicles to understand the surrounding environments and accordingly execute driving activities.…”

Secure and Efficient Blockchain based Knowledge Sharing for Intelligent Connected Vehicles

“…Rula 𝑒𝑡.𝑎𝑙 proposed a data fusion algorithm to realize vehicular context awareness [7]. A joint rate control and resource allocation scheme was developed with the information of channel states and delay constraints [8]. During the information sharing process, it is crucial to guarantee the security and reliability, which has not been addressed in these works.…”