Resilience Evaluation of Recognition and Planning Approaches in Cooperative Interacting Vehicles with Respect to Unexpected Disturbances (RESIST)

全自动和自动驾驶汽车的功能安全是未来几年的主要挑战之一。全自动驾驶汽车不仅要在理想条件下保持安全驾驶状态，还要在不可预见的情况下保持安全驾驶状态。合作互动策略的使用使确保对这些不可预见的情况和意想不到的干扰有足够的弹性变得更加复杂。为了使一辆汽车具备符合ISO 26262标准的全自动驾驶功能，目前它必须在道路上完成10亿公里的测试。该项目提案的目的是将应用程序的重要部分推进到基于模拟的验证过程，而不是真正的测试驱动，以便实现早期的弹性评估。与实际驾驶相比，其优势在于可以在各种参数下探索各种环境条件，从而发现特定的边界情况，此外还可以节省大量时间和成本。研究了不同环境条件和传感器影响下的协同感知方法及其弹性评价。协同感知方法应弥补不同算法和传感器类型在不同环境条件下识别率的降低。除了摄像传感器外，雷达传感器也将包括在调查中，并通过跨车辆目标跟踪加以扩展。复原力评价是由难以建模的进一步环境条件补充的。对喷雾、降雪和雾的各种参数进行建模和模拟的新方法有待研究。此外，在不同的驾驶场景下，研究了不同环境条件对感知、预测和规划算法的影响。随后，检查合适的度量标准以评估流程。一方面，评估了跨车融合的质量，另一方面，开发了一种安全度量，不仅解决了检测的精度，而且还解决了检测到的物体对车辆本身的危险程度。进一步的目标是在不同环境条件下持续改进基于学习的感知过程的训练，因为目前的训练数据集通常是理想条件下的记录，无法在实际操作中假设阳光。因此，我们希望系统地扩展数据集，并借助上述过程用这些更大的数据集训练相应的神经网络。

Functional safety of fully automated and autonomous vehicles is one of the main challenges of the upcoming years. A fully automated vehicle must not only remain in a safe driving state under ideal conditions, but also in the event of unforeseen situations. The use of cooperatively interacting strategies further complicates ensuring sufficient resilience against these unforeseeable situations and unexpected disturbances. To qualify a vehicle with fully automated driving functions in accordance with ISO 26262, it currently has to complete one billion test kilometers on the road. The aim of this project proposal is to advance a significant portion of the application to a simulation-based verification process instead of real test drives in order to enable early resilience evaluation. The advantages over real driving is the possibility of exploring a wide variety of environmental conditions in their variety of parameters in order to uncover specific borderline situations in addition to considerable time and cost savings. Cooperative perception methods and their resilience evaluation under varying environmental conditions and sensor influences are to be researched.The cooperative perception methods should compensate the reduced recognition rates of different algorithms and sensor types under different environmental conditions. In addition to the camera sensors, radar sensors are to be included in the investigations and extended by cross-vehicle object tracking. The resilience evaluation is supplemented by further environmental conditions that are difficult to model. New approaches are to be researched to model and simulate the variety of parameters of spray, snowfall and fog. In addition, the effects of different environmental conditions on perception, prediction and planning algorithms are investigated under different driving scenarios.Subsequently, suitable metrics are examined to evaluate the procedures. On the one hand, the quality of the cross-vehicle fusion is evaluated, and on the other hand, a safety metric is developed that not only addresses the precision of the detection, but also how dangerous a detected object could become for the vehicle itself.A further goal is the sustainable improvement of the training of learning-based perception procedures under varying environmental conditions, since currently the training data sets are usually recordings under ideal conditions and sunshine cannot be assumed in real operation. Therefore, we want to systematically extend the data sets and train the corresponding neural networks with these larger data sets with the help of the procedures mentioned above.