Latest Articles for Transportation Technology

A new approach to AV localization focuses on flexibility and safety.

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

SOGDet improves object detection by considering environmental context for autonomous driving.

Research shows music affects robot interaction through emotional cues.

LK-99 could change the future of superconductivity if proven effective.

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

MBAPPE enhances decision-making for autonomous vehicles using learned behaviors.

LineMarkNet enhances autonomous vehicle parking accuracy with advanced line detection.

New methods enhance autonomous driving by focusing on skill-based planning.

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

A new method for managing electric vehicle charging using multi-agent reinforcement learning.

This article discusses the benefits of time division multiplexing in connected vehicles.

Researchers find ways to safeguard sensitive data in cooperative agent environments.

Research reveals how gestures can enhance safety for pedestrians and self-driving cars.

Research focuses on improving explanations and handling uncertainty in AV decision-making.

New model helps self-driving cars merge safely into busy traffic.

A new method improves data handling in intelligent vehicles.

Improving the ability of AVs to predict driver behavior enhances safety.

Introducing a new model for better predicting vehicle movements in traffic.

Revolutionizing data sharing and safety in vehicle networks with new learning frameworks.

Exploring decision-making and explainability in self-driving cars.

Understanding self-driving cars is key to trust and safety.

UniV2X enhances autonomous driving through vehicle-infrastructure collaboration.

This article discusses how MBDRL optimizes flow control with efficiency and reduced costs.

New methods improve safety by predicting out-of-sight pedestrian movements for autonomous vehicles.

Innovative models enhance road condition understanding for autonomous vehicles.

New model improves vehicle environment recognition using cameras and LiDAR.

A new model enhances traffic predictions for smarter urban mobility.

M3Net enhances LiDAR segmentation for self-driving cars by integrating diverse datasets and sensors.

New methods improve control of turbulent separation bubbles for better vehicle efficiency.

BadFusion uses camera data to launch backdoor attacks on self-driving systems.

Combining LiDAR and camera data to improve self-driving technology efficiency.

A new approach enhances self-driving cars by mimicking human thinking patterns.

A new toolkit aids collaboration in designing pedestrian-vehicle interfaces.

UAD method reduces data needs and enhances efficiency in autonomous driving.

LCSim offers improved tools for realistic urban traffic scenarios.

Self-driving cars use rule-based systems for safer road navigation and decision-making.

Efficient controller design for uncertain systems using abstraction techniques.

SeFlow enhances motion estimation for self-driving cars using self-supervised learning.

This paper explores ways to better assess model calibration and predictive accuracy.