53 citations · 62 across the 12 of their papers we have counts for
6 papers · 1 filter
An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios
Leandro Parada, Hanlin Tian, Jose Escribano +1
Collaborative navigation becomes essential in situations of occluded scenarios in autonomous driving where independent driving policies are likely to lead to collisions. One promis…
Towards a Universal Evaluation Model for Careful and Competent Autonomous Driving
Kethan Reddy, Elias Nassif, Panagiotis Angeloudis +2
Virtual scenario-based testing methods to validate autonomous driving systems are predominantly centred around collision avoidance, and lack a comprehensive approach to evaluate op…
Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation
Hanlin Tian, Kethan Reddy, Yuxiang Feng +3
This paper introduces CRITICAL, a novel closed-loop framework for autonomous vehicle (AV) training and testing. CRITICAL stands out for its ability to generate diverse scenarios, f…
Safe and Efficient Manoeuvring for Emergency Vehicles in Autonomous Traffic using Multi-Agent Proximal Policy Optimisation
Leandro Parada, Eduardo Candela, Luis Marques +1
Manoeuvring in the presence of emergency vehicles is still a major issue for vehicle autonomy systems. Most studies that address this topic are based on rule-based methods, which c…
Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Real
Eduardo Candela, Leandro Parada, Luis Marques +3
Autonomous Driving requires high levels of coordination and collaboration between agents. Achieving effective coordination in multi-agent systems is a difficult task that remains l…
Quantitative Risk Indices for Autonomous Vehicle Training Systems
Eduardo Candela, Yuxiang Feng, Panagiotis Angeloudis +1
The development of Autonomous Vehicles (AV) presents an opportunity to save and improve lives. However, achieving SAE Level 5 (full) autonomy will require overcoming many technical…