Machine learning for social coordination of autonomous vehicles with human road users

机器学习用于自动驾驶车辆与人类道路使用者的社会协调

• 批准号: 2887445
• 金额: --
• 依托单位: University of Leeds
• 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-2887445
• 关键词: Machine; learning; social; coordination; autonomous

As part of the research activities in the Institute for Transport Studies (ITS) toward sustainable transportation systems and improving the safety of pedestrians and, the research will focus on understanding the social interaction of pedestrians and vulnerable human road users with automated vehicles (AVs) to improve the safety of urban traffic and the lower the rate of accidents due to false decision making and manoeuvre by the AVs. Using publicly available datasets and the University of Leeds driving simulator data, the research tries to understand pedestrians' behaviour in traffic scenes, enhance the accuracy of the existing machine vision-based models and contributes to the academic research gaps in the field; hence improving the performance and safety of automated driving functions. The student may also collaborate with other researchers in ITS with a background in psychology and human factor for a better understanding of vehicle/pedestrian interactions. All research activities in our human factors and safety research group at ITS Leeds aim at one common objective: maintaining resilient and safer transportation systems while reducing the number of traffic accidents, fatalities, and injuries.

作为运输研究所（ITS）的研究活动的一部分，对可持续运输系统并提高了行人的安全性，该研究将集中于了解行人和易受伤害的人类公路使用者的社交互动（AVS），以提高城市交通的安全性，以提高城市交通的安全性以及由于AVS的错误决策和机动而导致的误差率较低。该研究使用公开可用的数据集和利兹大学驱动模拟器数据，试图了解行人在交通场景中的行为，提高了现有的基于机器视觉的模型的准确性，并有助于该领域的学术研究差距；因此，提高自动驾驶功能的性能和安全性。该学生还可以在心理学和人为因素方面与其他研究人员合作，以更好地了解车辆/行人互动。我们在利兹的人为因素和安全研究小组中的所有研究活动都旨在实现一个共同的目标：维持弹性，更安全的运输系统，同时减少交通事故，死亡和伤害的数量。