An integrated ADAS for assessing risky situations in urban driving

Thomas H. Weisswange, B. Bolder, J. Fritsch, Stephan Hasler, C. Goerick
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引用次数: 8

Abstract

Advanced Driver Assistance Systems (ADAS) are becoming more and more popular. Many of these systems though are limited to specific scenes and often detect risky situations very late so they can only mitigate accidents. These effects are mainly caused by the use of simple physical prediction methods, e.g. to estimate the time-to-contact with another vehicle. In this paper we show an ADAS that extends the functionality of physical collision warnings by additionally estimating potential risks based on implicit predictions. As an example we demonstrate the use of vehicle orientation information for classifying situations. Through this, we can in particular assess the risk of static cars, for which physical prediction does not apply, but which can nevertheless easily cause an accident if they start moving into our driving corridor. The proposed system is evaluated online in a test car and is shown to reliably detect classical risky situations as well as those involving static cars.
用于评估城市驾驶危险情况的集成ADAS
高级驾驶辅助系统(ADAS)越来越受欢迎。然而,许多此类系统仅限于特定场景,通常很晚才发现危险情况,因此只能减轻事故。这些影响主要是由使用简单的物理预测方法造成的,例如估计与另一辆车接触的时间。在本文中,我们展示了一种扩展了物理碰撞警告功能的ADAS,它通过基于隐式预测来额外估计潜在风险。作为一个例子,我们演示了使用车辆方向信息对情况进行分类。通过这种方法,我们可以特别评估静态汽车的风险,对于这种情况,物理预测并不适用,但如果它们开始进入我们的驾驶通道,就很容易造成事故。该系统在一辆测试车上进行了在线评估,并被证明能够可靠地检测出经典的危险情况以及涉及静态汽车的危险情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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