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.

自动驾驶车辆 (AV) 与行人之间的互动是城市交通和道路安全的一个重要方面。这项研究的目标是开发能够与行人、骑自行车者等其他弱势道路使用者 (VRU) 以及其他道路车辆进行非平凡互动的自动驾驶汽车。目标是开发这样的系统，以便它们可以在可能涉及高风险的情况下运行，即时间或空间受限的情况，或者场景依赖于交互的情况，例如在十字路口或斑马线。为了实现这一点，自动驾驶车辆系统 (ADS) 需要先进的预测模型，能够准确预测每次交互中涉及的其他智能体的决策过程。有了这样的预测模型，我们需要为依赖交互的场景设计新颖的交互决策模型。本研究旨在解决这里出现的两个关键技术问题：（1）如何在决策中纳入复杂的行为模型，（2）如何纳入风险，例如来自不确定性估计的风险。此外，自动驾驶汽车的预测模型必须考虑其他智能体在交互过程中如何感知和推断自动驾驶汽车自己的行为和意图。从本质上讲，ADS 不仅必须全面了解另一方的决策，还必须全面了解两方或多方之间的意图和响应的相互建模，以确保道路上顺利、安全的交互。将准确而复杂的行为模型集成到 ADS 的决策过程中将取决于对动态环境中人类行为和其他智能体行为的透彻理解。这通常是个人主义的，同时也依赖于环境。有效捕获和处理这些复杂的信息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，还应该适应实时变化和意外场景。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高效的自主运行中发挥着至关重要的作用。特别是对于行人，本次讨论将讨论身体姿势作为非语言通信信号的相关性及其对自动驾驶车辆解释行人行为的效用。这项研究将利用最先进的计算机视觉方法进行人体姿势估计和跟踪，作为行人意图的潜在问题。纳入和管理风险，特别是那些源于自治系统环境的不确定性的风险至关重要。不确定性可能来自多种来源，例如传感器限制、恶劣天气条件或环境中不可预见的遮挡。有效的风险管理不仅涉及识别潜在的不确定性来源，还涉及量化它们并将其纳入决策过程。这就需要开发稳健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。这实际上将允许安全优先。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功能和可靠性至关重要，使它们能够应对复杂的现实场景，同时确保乘客及其在道路上互动的人的安全。