most citedA Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environment

1 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions

Haicheng Liao, Haoyu Sun, Huanming Shen +6

Accurately and promptly predicting accidents among surrounding traffic agents from camera footage is crucial for the safety of autonomous vehicles (AVs). This task presents substan…

cs.AI2024

Less is More: Efficient Brain-Inspired Learning for Autonomous Driving Trajectory Prediction

Haicheng Liao, Yongkang Li, Zhenning Li +10

Accurately and safely predicting the trajectories of surrounding vehicles is essential for fully realizing autonomous driving (AD). This paper presents the Human-Like Trajectory Pr…

cs.RO20241 cited

A Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environment

Haicheng Liao, Zhenning Li, Chengyue Wang +6

As autonomous driving technology progresses, the need for precise trajectory prediction models becomes paramount. This paper introduces an innovative model that infuses cognitive i…

cs.RO20241 cited

Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments

Haicheng Liao, Shangqian Liu, Yongkang Li +5

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often…

cs.AI20241 cited

A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving

Haicheng Liao, Yongkang Li, Zhenning Li +4

In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency. Incorpor…