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

虽然最近将单车辅助驾驶提升为自动驾驶已被许多汽车制造商提上发展议程，但专家们的共同理解是，只有考虑车辆合作，自动驾驶的预期优势才能充分发挥出来：提高交通流量，避免交通拥堵和事故，以及将乘客从监管任务中解脱出来，确实需要自动和非自动交通参与者之间的互动和合作。这一要求提出了两个主要挑战：(I)及时为涉及的汽车确定一套商定的驾驶计划，以便实现驾驶目标并适当考虑环境；(Ii)通过排除可能导致与任何交通参与者相撞的行为来确保安全，特别是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，我们开发了一个层次化的体系结构，结合行为预测，计算安全驾驶选项，并利用拍卖理论的原理建立合作驾驶策略。通过基于模拟的评估，这一解决方案概念已在选定的若干场景中获得成功。然而，测试也导致了这样一种认识，即为了实现本质安全同时又实用可靠的协作自动驾驶解决方案，有必要进行进一步的开发。第二阶段(本提案要求提供资金)将特别针对以下开放问题：(A)确保合作驾驶策略在需要时及时可用，即，计划生成的离线和在线部分交错，从而始终可以获得安全驾驶策略；(B)通过建立模型一致性和对不确定性的稳健性，说明用于规划的模型与真实车辆动力学之间的不匹配；(C)考虑到自动化车辆可能使用不同的合作概念和指标，即，我们为不同合作机制的车辆协议定义了共同的基础；以及(D)不仅通过模拟，而且在真实的自动化车辆上测试开发的架构；因此，将确定具有挑战性的场景，并将在德国航天中心的三辆全自动车辆上评估我们的计划和控制层次的实施。分别用于行为预测和合作规划的软件以及自动化车辆的接口将提供给参与SPP 1835的完整联合体。

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.