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

协作交互车辆的识别和规划方法对意外干扰的弹性评估（RESIST）

• 批准号: 273397906
• 负责人: Professor Dr. Oliver Bringmann
• 金额: --
• 依托单位: Fachbereich IV: Informatik
• 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 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/273397906?language=en
• 关键词: Resilience; Evaluation; Recognition; Planning; Approaches

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.

全自动和自动驾驶车辆的功能安全是未来几年的主要挑战之一。全自动车辆不仅要在理想条件下保持安全行驶状态，而且要在发生不可预见的情况时保持安全行驶状态。使用合作互动策略使确保针对这些不可预见的情况和意外干扰有足够的弹性变得更加复杂。为了使具有全自动驾驶功能的车辆符合 ISO 26262 标准，目前必须完成 10 亿公里的道路测试。该项目提案的目的是将应用程序的很大一部分推进到基于模拟的验证过程，而不是真正的试驾，以便能够进行早期弹性评估。与真实驾驶相比，其优点在于可以探索各种环境条件的各种参数，以发现特定的边界情况，此外还可以节省大量时间和成本。研究协同感知方法及其在不同环境条件和传感器影响下的弹性评估。协同感知方法应补偿不同算法和传感器类型在不同环境条件下识别率的降低。除了摄像头传感器之外，雷达传感器也将被纳入调查范围，并通过跨车辆目标跟踪进行扩展。难以建模的其他环境条件对复原力评估进行了补充。将研究新的方法来建模和模拟喷雾、降雪和雾的各种参数。此外，还研究了不同驾驶场景下不同环境条件对感知、预测和规划算法的影响。随后，检查合适的指标来评估程序。一方面，评估跨车辆融合的质量，另一方面，开发安全度量，不仅解决检测的精度，还解决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程序的训练，因为目前训练数据集通常是理想条件下的记录，在实际操作中无法假设阳光。因此，我们希望借助上述过程，系统地扩展数据集，并用这些更大的数据集训练相应的神经网络。