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：道路上检测先前未见、意外出现的障碍物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普通物体不同，检测道路上的意外障碍物无法仅通过升级传感器技术（如高分辨率视频成像仪/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，丢失的行李（如行李箱或自行车）、轮胎碎片、树干）。此外，增加物体类别或将所有这些物体归类为统一的“意外障碍物”类别并不具备可扩展性。在本研究中，我们探讨了使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，通过逆向问题：不是检测先前未见物体，而是AI自我廊道检测前方自我车道是否终止。一种智能的真值定义方法使得基于特征的简单分类能够识别自我车道的突然终止。我们提出了两种神经网络设计，并研究了使用合成数据进行训练的潜力等因素。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米的距离内检测到众多先前未见的障碍物，检测率达到95%。