Interactive Decision-Making Processes for Autonomous Driving Vehicle Systems.

自动驾驶汽车（AV）和行人之间的互动是城市交通和道路安全的一个关键方面。本研究的目标是开发能够与行人、其他弱势道路使用者（VRU）（如骑自行车的人）以及其他道路车辆进行非平凡交互的自动驾驶汽车。目的是开发这种系统，以便它们能够在可能涉及高风险的情况下运行，即在时间或空间有限的情况下，或在场景依赖于相互作用的情况下，例如在十字路口或斑马线上。为了实现这一点，自动驾驶汽车系统（ADS）需要先进的预测模型，可以准确地预测参与每次交互的其他代理或代理的决策过程。有了这样的预测模型，我们需要设计新的交互式决策模型的相互作用依赖的情况下。本研究旨在解决两个关键的技术问题，出现在这里：（1）如何将复杂的行为模型在决策中，（2）如何将风险，如不确定性估计。此外，它是至关重要的AV的预测模型，以占其他代理如何感知和推断AV自己的行动和意图在互动。从本质上讲，ADS不仅必须全面了解其他代理的决策，而且还必须全面了解双方或多方之间的意图和反应的相互建模，以确保道路上的顺利和安全互动。将准确和复杂的行为模型纳入ADS的决策过程将取决于对人类行为和动态环境中其他代理人的行动的透彻理解。这通常是个人主义的，同时也取决于上下文。有效地捕捉和处理这些复杂性需要开发先进的算法和机器学习技术。这些模型不仅应该能够识别不同的行为模式，还应该能够适应实时变化和意外情况。这涉及到深入研究人类决策的细微差别，包括社会和环境因素，这些因素在实现安全高效的自主运行方面发挥着至关重要的作用。特别是对于行人，本讨论将讨论身体姿势作为非语言通信信号的相关性及其在解释行人行为时对自动驾驶汽车的实用性。这项研究将利用最先进的计算机视觉方法进行人体姿态估计和跟踪，作为行人意图的潜在需求。消除和管理风险，特别是那些源于自治系统环境中的不确定性的风险至关重要。不稳定性可能来自各种来源，例如传感器限制、不利的天气条件或环境中不可预见的闭塞。有效的风险管理不仅涉及确定不确定性的潜在来源，而且还涉及将其量化并纳入决策过程。这就要求开发强大的不确定性估计技术和感知误差建模，以使ADS能够在给定的现实情况下进行训练。实际上，这将允许优先考虑安全。原则上，成功解决这两个关键挑战对于提高自动驾驶系统的能力和可靠性至关重要，使它们能够在复杂的现实世界场景中导航，同时确保乘客和他们在道路上互动的人的安全。

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.