3 citations · 3 across the 4 of their papers we have counts for
4 papers
Realistic adversarial scenario generation via human-like pedestrian model for autonomous vehicle control parameter optimisation
Yueyang Wang, Mehmet Dogar, Russell Darling +1
Autonomous vehicles (AVs) are rapidly advancing and are expected to play a central role in future mobility. Ensuring their safe deployment requires reliable interaction with other…
Realistic pedestrian-driver interaction modelling using multi-agent RL with human perceptual-motor constraints
Yueyang Wang, Mehmet Dogar, Gustav Markkula
Modelling pedestrian-driver interactions is critical for understanding human road user behaviour and developing safe autonomous vehicle systems. Existing approaches often rely on r…
Modeling Pedestrian Crossing Behavior: A Reinforcement Learning Approach with Sensory Motor Constraints
Yueyang Wang, Aravinda Ramakrishnan Srinivasan, Yee Mun Lee +1
Understanding pedestrian behavior is crucial for the safe deployment of Autonomous Vehicles (AVs) in urban environments. Traditional pedestrian behavior models often fall into two…
Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception
Yueyang Wang, Aravinda Ramakrishnan Srinivasan, Jussi P. P. Jokinen +2
This paper presents a model of pedestrian crossing decisions, based on the theory of computational rationality. It is assumed that crossing decisions are boundedly optimal, with bo…