【Abstract-1】Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles

Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles

Lost and Found：自动驾驶车辆的道路小危害检测

Peter Pinggera, Sebastian Ramos, Stefan Gehrig, Uwe Franke, Carsten Rother, Rudolf Mester

(Submitted on 15 Sep 2016)

Detecting small obstacles on the road ahead is a critical part of the driving task which has to be mastered by fully autonomous cars. In this paper, we present a method based on stereo vision to reliably detect such obstacles from a moving vehicle. The proposed algorithm performs statistical hypothesis tests in disparity space directly on stereo image data, assessing freespace and obstacle hypotheses on independent local patches. This detection approach does not depend on a global road model and handles both static and moving obstacles. For evaluation, we employ a novel lost-cargo image sequence dataset comprising more than two thousand frames with pixelwise annotations of obstacle and free-space and provide a thorough comparison to several stereo-based baseline methods. The dataset will be made available to the community to foster further research on this important topic. The proposed approach outperforms all considered baselines in our evaluations on both pixel and object level and runs at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery. Small obstacles down to the height of 5 cm can successfully be detected at 20 m distance at low false positive rates.

检测前方道路上的小障碍物是驾驶任务的关键部分，其必须完全由自动驾驶汽车控制。本文，我们提出了一种基于立体视觉，可靠地从运动车辆上检测到障碍物的方法。

该算法在视差空间直接对立体图像数据的统计假设检验，评估自由空间和独立局部分支的障碍物假设。这种检测方法不依赖于一个全局道路模型并能同时处理静态和移动障碍物。

为了评估该方法，我们采用了一种新的货物丢失图像序列数据集，由超过2000帧障碍标注和自由空间图像组成，并提供数据集在几个基于立体基线方法上的深入比较。该数据集将提供给社会，以促进该重要课题的进一步研究。

在高达20Hz的2百万像素的立体图像的帧速率运行，所提出的方法在基于像素和对象两个级别进行评估下，优于所有考虑的基准。在较低的假阳性概率下，可成功地检测在20米距离处高度为5厘米的小障碍。