Abstract
A significant challenge for real-world automated vehicles (AVs) is their interaction with human pedestrians. This paper develops a methodology to directly elicit the AV behaviour pedestrians find suitable by collecting quantitative data that can be used to measure and improve an algorithm's performance. Starting with a Deep Q Network (DQN) trained on a simple Pygame/Python-based pedestrian crossing environment, the reward structure was adapted to allow adjustment by human feedback. Feedback was collected by eliciting behavioural judgements collected from people in a controlled environment. The reward was shaped by the inter-action vector, decomposed into feature aspects for relevant behaviours, thereby facilitating both implicit preference selection and explicit task discovery in tandem. Using computational RL and behavioural-science techniques, we harness a formal iterative feedback loop where the rewards were repeatedly adapted based on human behavioural judgments. Experiments were conducted with 124 participants that showed strong initial improvement in the judgement of AV behaviours with the adaptive reward structure. The results indicate that the primary avenue for enhancing vehicle behaviour lies in the predictability of its movements when introduced. More broadly, recognising AV behaviours that receive favourable human judgments can pave the way for enhanced performance.