Machine Learning for Connected and Autonomous Vehicle for Mixed Traffic Environment

Dear Colleagues,

Connected and autonomous vehicles (CAVs) are an imperative emerging technology for the future intelligent transportation systems (ITSs). Early research shows that CAVs can alleviate vehicle collisions, fatalities, traffic congestion, and energy consumption, and can enhance driving comfort. However, undeniably, the future deployment of CAVs will be alongside the pre-existing human-driven vehicles (HDVs) in the legacy road and traffic infrastructure. There are many existing research challenges in CAVs, including reliable communication in intermittent vehicular networks, accurate vehicle state estimation, safe longitudinal and lateral movements, efficient vehicle routing, and energy efficiency, to name but a few. Mixed traffic introduces even more challenges.

Recently, machine learning (ML) has been used for classifying and training CAVs in different aspects. Both supervised and unsupervised ML algorithms are being used in solving different issues in CAVs. Certainly, there is still a huge research gap and opportunities to develop improved ML-based techniques to solve many existing challenges in CAVs. Some of the challenges of ML-based techniques for CAVs are efficient computation, neural architecture, reward functions design, adaptability, generalization, verification and validation, safety, etc. 

This Special Issue covers the following relevant areas (not an exhaustive list):

• Vehicle trajectory prediction;
• Driving behavior prediction;
• Safe and efficient longitudinal and lateral movements;
• Route planning in mixed traffic;
• Vehicle routing in mixed traffic;
• Reliable vehicular communications;
• Edge/fog-computing-assisted traffic management;
• Driving CAVs under legacy road and traffic situations;
• Vehicle clustering;
• Vehicle state estimation;
• CAV communication technologies (WiFi/DSRC/5G LTE/cognitive communications);
• Efficient ML (deep learning, reinforcement learning, etc.)-based CAV computation and architecture;
• Co-operative and distributed federated learning for CAVs;
• ML-assisted security and privacy in CAVs;
• Energy efficiency in mixed traffic.

Dr. G. G. Md. Nawaz Ali
Prof. Dr. Peter Han Joo Chong
Guest Editors

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