Interactive Decision-Making Processes for Autonomous Driving Vehicle Systems.

自动驾驶车辆系统的交互式决策过程。

• 批准号: 2878901
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2878901
• 关键词: Interactive; Decision; Making; Processes; Autonomous

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.

自动驾驶汽车（AV）与行人之间的相互作用是城市流动性和道路安全的关键方面。这项研究的目的是开发可以与行人，其他易受伤害的道路使用者（VRU）以及其他公路车辆进行非试验的AVS。目的是开发此类系统，以便它们可以在可能涉及高风险的情况下操作，即时间或空间的限制或场景取决于相互作用的地方，例如在交叉点或斑马交叉点。为此，可行的是，自动驾驶车辆系统（ADS）需要高级预测模型，这些模型可以准确地预测每种交互所涉及的其他代理商或代理的决策过程。通过这样的预测模型，我们需要为互动依赖性方案设计新颖的交互式决策模型。这项研究的目的是解决此处出现的两个关键技术问题：（1）如何将复杂的行为模型纳入决策，（2）如何合并风险，例如不确定性估计。此外，AV的预测模型必须说明其他代理在交互过程中如何看待和推断AV自身的行为和意图。从本质上讲，广告不仅必须对其他代理人的决策，而且对两个或多个政党之间的意图和回应的相互建模，以确保道路上的平稳且安全的互动。将准确而复杂的行为模型整合到AD的决策过程中将取决于对人类行为的透彻理解以及在动态环境中其他代理的行为。这通常是个人主义的，同时也取决于上下文。有效地捕获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各种行为模式，还应适应实时变化和意外情况。这涉及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有效的自主行动中起着至关重要的作用。对于行人而言，该讨论将解决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动时对自动驾驶汽车的效用。这项研究将利用最先进的计算机视觉方法来进行人姿势估计和跟踪作为行人意图的潜在Que。纳入和管理风险，尤其是那些因自主系统环境中不确定性而引起的风险至关重要。不确定性可能来自各种来源，例如传感器局限性，不利天气条件或环境中无法预料的阻塞。有效的风险管理不仅涉及确定不确定性的潜在来源，还涉及将其量化并纳入决策过程。这要求开发强大的不确定性估计技术和感知误差建模，以使广告能够训练给定的现实情况。这将有效，可以优先考虑安全。原则上，这两个关键挑战的成功解决对于促进自主系统的能力和可靠性至关重要，使他们能够在确保乘客的安全及其在道路上互动的人的安全性，同时驾驶复杂的现实世界情景。