11 citations · 20 across the 6 of their papers we have counts for
6 papers
Arrive and Survive: Scaling Safe Goal-Conditioned Policy Learning from One-Bit Failure Signals
Guopeng Li, Yiyang Duan, Yiru Jiao +1
Contrastive reinforcement learning (CRL) scales effectively in goal-conditioned tasks by casting policy learning into a self-supervised contrastive objective. However, in a failure…
Learning collision risk proactively from naturalistic driving data at scale
Yiru Jiao, Simeon C. Calvert, Sander van Cranenburgh +1
Accurately and proactively alerting drivers or automated systems to emerging collisions is crucial for road safety, particularly in highly interactive and complex urban environment…
Structure-preserving contrastive learning for spatial time series
Yiru Jiao, Sander van Cranenburgh, Simeon Calvert +1
The effectiveness of neural network models largely relies on learning meaningful latent patterns from data, where self-supervised learning of informative representations can enhanc…
A Unified Probabilistic Approach to Traffic Conflict Detection
Yiru Jiao, Simeon C. Calvert, Sander van Cranenburgh +1
Traffic conflict detection is essential for proactive road safety by identifying potential collisions before they occur. Existing methods rely on surrogate safety measures tailored…
A Conflict Resolution Dataset Derived from Argoverse-2: Analysis of the Safety and Efficiency Impacts of Autonomous Vehicles at Intersections
Guopeng Li, Yiru Jiao, Simeon C. Calvert +1
As the deployment of autonomous vehicles (AVs) in mixed traffic flow becomes increasingly prevalent, ensuring safe and smooth interactions between AVs and human agents is of critic…
Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles
Guopeng Li, Yiru Jiao, Victor L. Knoop +2
Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently w…