Latest Articles for Autonomous Vehicles

New model improves object recognition for enhanced road safety.

A new method improves how self-driving cars recognize and manage unexpected road challenges.

A new method enhances AV models' adaptability to camera viewpoint changes.

FusionFormer improves 3D object detection by effectively combining camera and LiDAR data.

A new method using federated learning for large-scale mapping with neural radiance fields.

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

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

ImageTo360 improves LiDAR segmentation using minimal labeled data from camera images.

A new approach enhances object detection accuracy using single-camera systems.

DEFormer improves image quality in low-light conditions using frequency data.

Research focuses on reducing motion bias in SLAM for better reliability.

Budding Ensemble Architecture enhances object detection reliability and accuracy.

CAVs are becoming safer with smart infrastructure for better traffic movement predictions.

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

This article discusses new methods for improving task offloading in self-driving cars.

A new method integrates physics and deep learning to predict human movement accurately.

This article discusses the role of BEV perception in self-driving technology.

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

Exploring how language models enhance self-driving car communication and decision-making.

Robots can better adapt to new terrains by predicting operator preferences.

A new method improves robot movement models on various surfaces.

Parking Pedestrian Dataset improves safety in self-driving cars by focusing on pedestrian detection.

Examining how surface reflectivity affects sensor performance in autonomous vehicles.

LineMarkNet enhances autonomous vehicle parking accuracy with advanced line detection.

Research shows how simplifying data use improves self-driving vehicle performance.

OccluTrack enhances tracking accuracy by addressing occlusion challenges in crowded environments.

A new method enhances vehicle positioning in challenging environments using multiple sensors.

New framework enhances camera localization accuracy using multi-view constraints and LiDAR data.

A method for efficient traffic sign classification in autonomous driving.

A novel control method enhances coordination of underwater vehicles for various tasks.

Examining how windshields affect the perception capabilities of self-driving cars.

A new technique enhances the accuracy of GPS and VIO for better localization.

DJINN generates realistic traffic scenarios for testing self-driving cars.

A method to improve object detection in varied environments using past data.

Examining control methods to improve traffic flow with autonomous and human-driven vehicles.

FGFusion improves 3D object detection by combining lidar and camera data effectively.

Innovative approach improves image recognition testing efficiency and accuracy.

Exploring how AVs can communicate their future actions for safer driving.

New framework SurrealDriver improves driver agent simulation for urban environments.

Integrating reinforcement learning and grammar for better camera designs.