In machine learning (ML), a widespread adage is that the area under the precision-recall
curve (AUPRC) is a superior metric for model comparison to the area under the receiver …

Vehicle trajectory anomaly detection plays an essential role in the fields of traffic video
surveillance, autonomous driving navigation, and taxi fraud detection. Deep generative …

Koopman operator theory offers a rigorous treatment of dynamics and has been emerging
as a powerful modeling and learning-based control method enabling significant …

In recent years there have been remarkable advancements in autonomous driving. While
autonomous vehicles demonstrate high performance in closed-set conditions, they …

Traditional highway safety and monitoring solutions, reliant on surveillance cameras, face
limitations due to their dependence on high-speed internet connectivity and the remote …

Abstract Machine learning models for near collision detection in autonomous vehicles
promise enhanced predictive power. However, training on these large datasets presents …

Despite the significant research efforts on trajectory prediction for automated driving, limited
work exists on assessing the prediction reliability. To address this limitation we propose an …

Autonomous vehicles heavily rely on various sensors to evaluate their surroundings and
issue essential control commands. Nonetheless, these sensors are susceptible to false data …

As autonomous driving technology continues to advance and gradually become a reality,
ensuring the safety of autonomous driving in complex traffic scenarios has become a key …

We investigate the feasibility of deploying reinforcement learning (RL) policies for
constrained crowd navigation using a low-fidelity simulator. We introduce a representation of …