The detection of previously unseen, unexpected obstacles on the road is a major challenge for automated driving systems. Different from the detection of ordinary objects with pre-definable classes, detecting unexpected obstacles on the road cannot be resolved by upscaling the sensor technology alone (e.g., high resolution video imagers / radar antennas, denser LiDAR scan lines). This is due to the fact, that there is a wide variety in the types of unexpected obstacles that also do not share a common appearance (e.g., lost cargo as a suitcase or bicycle, tire fragments, a tree stem). Also adding object classes or adding \enquote{all} of these objects to a common \enquote{unexpected obstacle} class does not scale. In this contribution, we study the feasibility of using a deep learning video-based lane corridor (called \enquote{AI ego-corridor}) to ease the challenge by inverting the problem: Instead of detecting a previously unseen object, the AI ego-corridor detects that the ego-lane ahead ends. A smart ground-truth definition enables an easy feature-based classification of an abrupt end of the ego-lane. We propose two neural network designs and research among other things the potential of training with synthetic data. We evaluate our approach on a test vehicle platform. It is shown that the approach is able to detect numerous previously unseen obstacles at a distance of up to 300 m with a detection rate of 95 %.

翻译：道路上未知且意外的障碍物检测是自动驾驶系统面临的主要挑战。与检测具有预定义类别的常规物体不同，检测道路上意外障碍物无法仅通过提升传感器技术（如高分辨率视频成像器/雷达天线、更密集的激光雷达扫描线）来解决。这是因为意外障碍物类型多样且缺乏共同外观特征（例如遗失货物如行李箱或自行车、轮胎碎片、树干）。同时，增加物体类别或将所有此类物体归入统一的“意外障碍物”类别并不具备可扩展性。在本研究中，我们探讨了利用基于深度学习的视频车道廊道（称为“AI自我廊道”）来简化这一挑战的可行性，其核心思路是反转问题：不再检测先前未见过的物体，而是通过AI自我廊道检测本车道前方是否终止。通过精巧的真值定义，我们实现了对车道突然终止的简易特征分类。我们提出了两种神经网络架构，并研究了使用合成数据进行训练的可能性等课题。我们在测试车辆平台上评估了该方法。结果表明，该方法能够在高达300米的距离上检测多种先前未见的障碍物，检测率达到95%。