Interactive Decision-Making Processes for Autonomous Driving Vehicle Systems.

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The interaction between Autonomous Vehicles (AVs) and pedestrians is a critical aspect of urban mobility and road safety. The objective of this research is to develop AVs which can nontrivially interact with pedestrians, other vulnerable road users (VRUs) such as cyclists, as well as other road vehicles. The objective is to develop such systems so that they can operate in situations that could involve high risk, i.e. where time or space is constrained, or where the scenario is dependent on interaction, such as at intersections or zebra crossings. For this to be feasible, the autonomous driving vehicle system (ADS) requires advanced predictive models that can accurately anticipate the decision-making process of the other agent or agents involved in each interaction. With such predictive models, we need to devise novel interactive decision-making models for interaction-dependent scenarios. This study will aim to resolve two key technical issues which arise here: (1) how to incorporate complex behavioural models in decision making, (2) how to incorporate risk, such as from uncertainty estimation. Furthermore, it is essential for the AV's predictive models to account for how other agents perceive and infer the AV's own actions and intentions during the interaction. In essence, the ADS must possess a comprehensive understanding of not only the other agent's decision-making but also the reciprocal modelling of intentions and responses between the two or more parties to ensure smooth and safe interactions on the road. Integrating accurate and complex behavioural models into the decision-making processes of ADS will depend on a thorough understanding of human behaviour and the actions of other agents in dynamic environments. This is often individualistic while also context dependent. Effectively capturing and processing these intricacies requires the development of advanced algorithms and machine learning techniques. These models should not only be capable of recognising diverse behavioural patterns but also adapt to real-time changes and unexpected scenarios. This involves delving into the nuances of human decision-making, including both social and environmental factors, which play a crucial role in achieving safe and efficient autonomous operation. For pedestrians specifically, this discussion will address the relevance of body posture as a non-verbal communication signal and its utility for autonomous vehicles in interpreting pedestrian actions. This study will leverage state-of-the-art computer vision methods for human pose estimation and tracking as a potential que for pedestrian intent. Incorporating and managing risk, particularly those stemming from uncertainty in the autonomous system's environment is crucial. Uncertainties can emerge from various sources, such as sensor limitations, adverse weather conditions, or unforeseen occlusions in the environment. Efficient risk management involves not only identifying potential sources of uncertainty but also quantifying and incorporating them into the decision-making process. This calls for the development of robust uncertainty estimation techniques and perception error modelling, to allow the ADS to train given realistic circumstances. This will in effect, allow for the prioritisation of safety. In principle, the successful resolution of these two pivotal challenges is crucial for advancing the capabilities and reliability of autonomous systems, enabling them to navigate complex real-world scenarios while ensuring the safety of passengers and those they interact with on roads.