论文标题
a-eye：用AI的眼睛开车去拐角案例生成
A-Eye: Driving with the Eyes of AI for Corner Case Generation
论文作者
论文摘要
这项工作的总体目标是通过所谓的角案例丰富自动驾驶的训练数据。在道路交通中，拐角处是至关重要的，罕见和不寻常的情况，这些情况挑战了AI算法的看法。为此，我们介绍了测试钻机的设计，以使用人类在循环的方法生成合成角病例。对于测试台，将实时的语义分割网络训练并集成到驾驶模拟软件CARLA中，以使人可以按照网络的预测行驶。此外，第二个人可以从原始的Carla输出中看到相同的场景，并在语义驱动程序显示出危险的驾驶行为后立即在第二个控制单元的帮助下进行干预。干预措施可能表明分割网络对关键场景的认识不佳，然后代表角案。在我们的实验中，我们表明有针对性的培训数据具有角落病例，从而改善了道路交通中安全相关事件中的行人检测。
The overall goal of this work is to enrich training data for automated driving with so called corner cases. In road traffic, corner cases are critical, rare and unusual situations that challenge the perception by AI algorithms. For this purpose, we present the design of a test rig to generate synthetic corner cases using a human-in-the-loop approach. For the test rig, a real-time semantic segmentation network is trained and integrated into the driving simulation software CARLA in such a way that a human can drive on the network's prediction. In addition, a second person gets to see the same scene from the original CARLA output and is supposed to intervene with the help of a second control unit as soon as the semantic driver shows dangerous driving behavior. Interventions potentially indicate poor recognition of a critical scene by the segmentation network and then represents a corner case. In our experiments, we show that targeted enrichment of training data with corner cases leads to improvements in pedestrian detection in safety relevant episodes in road traffic.
