Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen
driving scenarios, largely stemming from the non-interpretability and poor generalization of …

Predicting the trajectories of surrounding objects is a critical task for self-driving vehicles and
many other autonomous systems. Recent works demonstrate that adversarial attacks on …

Enhancing roadway safety has become an essential computer vision focus area for
Intelligent Transportation Systems (ITS). As a part of ITS Vehicle Trajectory Prediction (VTP) …

Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …

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

The recent development of cloud computing and edge computing shows great promise for
the Connected and Automated Vehicle (CAV), by enabling CAVs to offload their massive on …

In autonomous driving, the most challenging scenarios can only be detected within their
temporal context. Most video anomaly detection approaches focus either on surveillance or …

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

Neural networks are being applied to a wide range of tasks in autonomous systems, such as
perception, prediction, planning, control, and general decision making. While they may …

The prediction module, powered by deep learning models, constitutes a fundamental
component of high-level Autonomous Vehicles (AVs). Given the direct influence of the …