Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning

The burgeoning navigation services using digital maps provide great convenience to drivers. However, there are sometimes anomalies in the lane rendering map images, which might mislead human drivers and result in unsafe driving. To accurately and effectively detect the anomalies, this paper transforms lane rendering image anomaly detection into...

Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning

Traffic scenarios in roundabouts pose substantial complexity for automated driving. Manually mapping all possible scenarios into a state space is labor-intensive and challenging. Deep reinforcement learning (DRL) with its ability to learn from interacting with the environment emerges as a promising solution for training such automated driving...

String Stability of Heterogeneous Platoons with Non-connected Automated Vehicles

It is expected that automated vehicles will gradually penetrate on public roads, resulting in mixed traffic in the next decades. This can impact traffic flow operations, especially the roadway capacity and flow stability. It is of paramount<br/>importance to understand and predict the implications of automated driving systems on traffic flow at...