Cooperative and Intrinsically-Correct Control of Vehicles in Diverse Environments (CoInCiDE)

不同环境中车辆的协作和本质正确控制 (CoInCiDE)

• 批准号: 273142721
• 负责人: Professor Dr.-Ing. Matthias Althoff
• 金额: --
• 依托单位: Institut für Verkehrssystemtechnik (TS)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/273142721?language=en
• 关键词: Cooperative; Intrinsically; Correct; Control; Vehicles

While advancing assisted driving for single cars to automated driving has been put on the development agenda of many car manufacturers recently, the common understanding of experts is that the anticipated advantages of automated driving will fully unfold only if vehicle cooperation is considered: The enhancement of traffic flow, the avoidance of traffic jams and accidents, as well as relieving passengers from the task of supervision does require interaction and cooperation between automated and non-automated traffic participants. This requirement poses two main challenges: (i) to determine an agreed set of driving plans for the involved cars timely, such that the driving goals are met and the environment is appropriately considered, (ii) to guarantee safety by excluding behaviors possibly leading to collisions with any traffic participant, and, in particular, to exploit cooperation to predict and avoid situations that are potentially dangerous. This project targets exactly these two challenges: In the first phase, we have developed a hierarchical architecture combining behavior prediction, computing safe driving options, and establishing cooperative driving strategies by using principles of auction theory. The success of this solution concept has been demonstrated for a selected number of scenarios by simulation-based evaluation. The tests have also lead to the insight, however, which further developments are necessary for realizing an intrinsically safe and at the same time practically sound solution to cooperative automated driving. The second phase (for which funding is requested by this proposal) will in particular target the open issues of: (a) guaranteeing that cooperative driving strategies are timely available when required, i.e., the offline and online parts of plan generation are interlaced such that a safe driving strategy is always available; (b) accounting for the mismatch between the models used for planning and the true vehicle dynamics by establishing model conformance and robustness to uncertainty, (c) considering that the automated vehicles may use different concepts and metrics for cooperation, i.e., we define a common basis for vehicle agreement over different cooperation mechanisms; and (d) testing the developed architecture not only by simulation but on real automated vehicles; hereto, challenging scenarios will be determined, and an implementation of our planning and control hierarchy will be evaluated on DLR's three fully automated vehicles. The respective software for behavior prediction and cooperative planning, as well as the interfaces to the automated vehicles will be made available to the complete consortium involved in the SPP 1835.

虽然将单汽车的辅助驾驶推进到自动驾驶近来已被许多汽车制造商提上了发展议程，但专家们的共识是，只有考虑到车辆的配合，自动驾驶的预期优势才能充分展现出来：增加交通流量，避免交通堵塞和事故，以及将乘客从监督任务中解脱出来，确实需要自动化和非自动化交通参与者之间的交互和合作。该要求提出了两个主要挑战：（i）及时确定所涉及的汽车的一组商定的驾驶计划，使得满足驾驶目标并且适当地考虑环境，（ii）通过排除可能导致与任何交通参与者碰撞的行为来保证安全，并且特别地，利用合作来预测和避免潜在危险的情况。该项目正是针对这两个挑战：在第一阶段，我们已经开发了一个分层架构，结合行为预测，计算安全驾驶选项，并建立合作驾驶策略，通过使用拍卖理论的原则。通过基于模拟的评估，已经证明了这种解决方案概念在选定的一些场景中的成功。然而，这些测试也让我们认识到，为了实现本质安全，同时又切实可行的协作自动驾驶解决方案，需要进一步的开发。第二阶段（本提案要求为此提供资金）将特别针对以下未决问题：（a）保证在需要时及时提供合作驾驶战略，即，计划生成的离线和在线部分是交错的，使得安全驾驶策略总是可用的;（B）通过建立模型一致性和对不确定性的鲁棒性来解决用于计划的模型和真实车辆动态之间的失配，（c）考虑到自动车辆可以使用不同的概念和度量进行协作，即，我们为不同合作机制的车辆协议定义了一个共同的基础;（d）不仅通过模拟，而且在真实的自动驾驶车辆上测试所开发的体系结构;至此，将确定具有挑战性的场景，并将在DLR的三辆全自动驾驶车辆上评估我们的规划和控制层次的实施。用于行为预测和合作规划的相应软件以及与自动驾驶车辆的接口将提供给SPP 1835中涉及的完整联盟。