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Found 10 papers, 5 papers with code
KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections
Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic management and autonomous driving systems.
LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs
Autonomous driving (AD) has made significant strides in recent years.
Driving through the Concept Gridlock: Unraveling Explainability Bottlenecks in Automated Driving
In the context of human-assisted or autonomous driving, explainability models can help user acceptance and understanding of decisions made by the autonomous vehicle, which can be used to rationalize and explain driver or vehicle behavior.
Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control
In the online component, driver feedback is used to update the driving gap preference in real time.
Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks
Accurate prediction of vehicle trajectories is vital for advanced driver assistance systems and autonomous vehicles.
CEMFormer: Learning to Predict Driver Intentions from In-Cabin and External Cameras via Spatial-Temporal Transformers
Driver intention prediction seeks to anticipate drivers' actions by analyzing their behaviors with respect to surrounding traffic environments.
M$^2$DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer
Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal.
Advances and Applications of Computer Vision Techniques in Vehicle Trajectory Generation and Surrogate Traffic Safety Indicators
This review is expected to assist transportation researchers and engineers with the selection of suitable CV techniques for video processing, and the usage of SSMs for various traffic safety research objectives.
Trajectory Prediction for Vehicle Conflict Identification at Intersections Using Sequence-to-Sequence Recurrent Neural Networks
A comparison between Time-to-Collision (TTC) conflict identification using vehicle bounding boxes versus the commonly used vehicle center points for geometric representation was conducted.
CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins
The development of safety-oriented research and applications requires fine-grain vehicle trajectories that not only have high accuracy, but also capture substantial safety-critical events.
