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Found 6 papers, 3 papers with code
Less is More: Efficient Brain-Inspired Learning for Autonomous Driving Trajectory Prediction
Accurately and safely predicting the trajectories of surrounding vehicles is essential for fully realizing autonomous driving (AD).
MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving
This paper introduces a trajectory prediction model tailored for autonomous driving, focusing on capturing complex interactions in dynamic traffic scenarios without reliance on high-definition maps.
BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving
The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles.
GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models
In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge.
MSAF: Multimodal Split Attention Fusion
Multimodal learning mimics the reasoning process of the human multi-sensory system, which is used to perceive the surrounding world.
Ranked #19 on
Action Recognition
on NTU RGB+D
A Spontaneous Driver Emotion Facial Expression (DEFE) Dataset for Intelligent Vehicles
In this paper, we introduce a new dataset, the driver emotion facial expression (DEFE) dataset, for driver spontaneous emotions analysis.
