In an effort to reach accident-free milestones or drastically reduce/eliminate road fatalities
rates and traffic congestion and to create disruptive, transformational mobility systems and …

Driving style may have an important effect on traffic safety. Proactive crash risk prediction for
lane-changing behaviors incorporating individual driving styles can help drivers make safe …

The vehicle platoon will be the most dominant driving mode on future roads. To the best of
our knowledge, few reinforcement learning (RL) algorithms have been applied in vehicle …

Proactive lane-changing (LC) risk prediction can assist driver's LC decision-making to
ensure driving safety. However, most previous studies on LC risk prediction did not consider …

Predicting lane change maneuvers is critical for autonomous vehicles and traffic
management as lane change may cause conflict in traffic flow. Most existing studies do not …

Lane-changing is a complicated task and has a high probability of accident occurrence.
Although a large body of literature has used vehicle trajectories to microscopically …

Traffic oscillations, also known as stop-and-go driving conditions, commonly emerge due to
the alternative deceleration and acceleration of vehicles in congested traffic conditions. This …

Crash frequency model is often subject to excessive zero observation because of the rare
nature of crashes. To address the problem of imbalanced crash data, a deep generative …

Road safety guidelines are not properly implemented and are not diverse enough to counter
an annual increase in traffic volume. The mitigation techniques of road regulating bodies …

The accuracy of the data is crucial to the real-time prediction of autonomous driving. Due to
factors such as weather and the accuracy of data collection equipment, there frequently exist …