Latest Articles for Autonomous Vehicles

A new method enhances visual odometry for underwater vehicles.

A new method improves detection of self-driving car model failures.

New methods enhance safety in self-driving cars amid unpredictable obstacles.

Revolutionizing lane detection for self-driving cars with enhanced accuracy and understanding.

New methods combine camera data with GPS for enhanced location accuracy.

A new method improves 3D semantic segmentation by integrating 2D techniques.

A new method improves detection of unknown objects in varied images.

Exploring ways to enhance place recognition using various sensor data.

This study examines how Gaussian blur affects object detection in simulated environments.

A new platform enhances testing for autonomous vehicles through realistic racing simulations.

This article discusses the role of Deformable Convolutions in enhancing semantic segmentation for autonomous driving.

Examining how sensor data timing affects autonomous vehicle safety and performance.

Evaluating large language models for enhancing road safety in self-driving cars.

A new approach improves how vehicles predict pedestrian actions.

New method improves accuracy and efficiency in 3D object detection using Linear Group RNN.

This study examines a method to enhance segmentation in varied driving conditions.

This article discusses the latest methods in video object detection.

A new model uses text to guide LiDAR data creation, reducing costs and improving accuracy.

A new system enhances predictions of nearby vehicle movements for self-driving cars.

A new approach to improve LiDAR mapping accuracy in robotics.

A new approach to analyze reachability in systems influenced by randomness.

RoCo framework addresses pose errors for better object detection in vehicle collaboration.

Multi-Task Grouping improves learning efficiency and performance across various AI applications.

New methods improve accuracy in tracking objects using only distance measurements.

A look at infrastructure-based solutions for better autonomous vehicle perception.

A new method enhances video forecasting by separating scene elements for better predictions.

New method enhances vehicle tracking using detailed 3D building models.

A new method improves 3D object detection using LiDAR data without labels.

V2INet improves trajectory prediction using multi-view data for autonomous vehicles.

RPrDepth uses single images for accurate depth estimation leveraging rich-resource data.

A powerful simulator designed to enhance self-driving car training through rapid scenario generation.

Combining existing methods improves OOD detection for safer machine learning applications.

SceneMotion improves predictions for self-driving cars by considering agent interactions.

VoxelTrack improves 3D tracking accuracy using voxel representation and real-time capabilities.

A small-scale city model aids research on self-driving cars and smart transport systems.

A new method improves object detection in autonomous vehicles using vision and language.

A new framework enhances robot safety and efficiency in unpredictable settings.

Introducing MS-Mapping, a system improving LiDAR mapping accuracy and efficiency.

Developing agents that engage in real-time dialogue for better navigation.

A new method improves robot behavior safety using STL specifications.