Latest Articles for Traffic Safety

This article examines how drivers decide when to overtake in mixed traffic.

A new method improves prediction of traffic movements for autonomous vehicles.

Using Deep Q-Learning to teach autonomous cars lane-keeping and collision avoidance.

A new framework clarifies behavior of autonomous agents by analyzing rule adherence.

A new method improves trajectory prediction accuracy without relying on high-definition maps.

This article examines how to assess the intentions of AI systems.

New approach improves vehicle movement predictions for autonomous driving systems.

A model for connected automated vehicles to improve traffic safety and efficiency.

Innovative infrastructure improves safety at busy urban junctions.

A fresh approach to make self-driving cars safer and more comfortable.

Introducing STZINB-GNN, a better model for predicting traffic accidents and uncertainties.

A new framework improves traffic accident detection accuracy in driving videos.

A new approach improves prediction accuracy and efficiency for self-driving cars in traffic.

A new approach enhances AV communication with human drivers during complex maneuvers.

A new method improves vehicle interactions for better driving safety.

Automated driving technology promises safer roads and reduced accidents.

This study reveals how drivers handle merging situations on the road.

A study examines how speeding habits differ globally and their impact on road safety.

A new method enhances future position predictions for autonomous vehicles.

A new method personalizes vehicle predictions to improve safety and driver assistance.

CLLD enhances lane detection under tough conditions using self-supervised learning.

A new method predicts driver behavior and enhances safety in hybrid traffic.

New features enhance how AVs learn driving styles for safer interactions.

A new model combines neural networks and choice models for safer driving predictions.

MapPrior improves BEV perception, enhancing accuracy and safety for autonomous vehicles.

Radar data accumulation can boost object detection in autonomous vehicles.

Enhancing driver understanding for safer semi-autonomous driving.

SKoPe3D provides synthetic data for vehicle keypoint detection in traffic systems.

Motion prediction is vital for safe autonomous vehicle operation, enhancing traffic flow and decision making.

Innovative methods improve the safety and accuracy of autonomous vehicle navigation.

Rank2Tell dataset enhances understanding of traffic scenes for self-driving technology trust.

Examining issues in detecting pedestrians to improve road safety for autonomous vehicles.

Research on detecting unusual objects in autonomous vehicles through advanced imaging techniques.

Exploring improvements in Reinforcement Learning for better autonomous driving.

This study explores using cameras to enhance vehicle-to-vehicle communication.

A look into estimating traffic accident likelihood using adaptive techniques.

Researchers analyze how AVs and human drivers manage conflicts at intersections.

A new model enhances lane-changing and overtaking of self-driving vehicles.

A new model enhances how self-driving cars predict pedestrian movements in crowded areas.

New methods improve safety in self-driving cars through better interaction modeling.