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Risk-based driver assistance for approaching intersections of limited visibility

机译：基于风险的驾驶员协助，可帮助您接近视野有限的十字路口

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摘要

This work addresses the general problem of risk evaluation in traffic scenarios for the case of limited observability of the scene due to a restricted sensory coverage. Here we especially concentrate on intersection scenarios, which are visually difficult to access. To distinguish the area of sight, we employ publicly available digital map data which includes, besides the general road geometry, information about buildings potentially blocking the driver's visibility. Based on the estimated area of sight, we augment the sensory perceived environment with potentially present, but not perceivable, critical scene entities. For those potentially present scene entities, we predict a, for the ego driver, worst-case-like behavior and evaluate the upcoming collision risk. This risk model can then be employed to enrich the traffic scene analysis with potentially upcoming hazards, which result from a restricted sensory coverage. Furthermore, it can be utilized to evaluate the driver's current behavior in terms of risk, warn the driver in case its current behavior is considered as critical and give suggestions on how to act in a risk-aversive way. By applying the resulting intersection warning system to real world scenarios, we could validate our approach. The proposed system's behavior reveals to be highly similar to the general behavior of a correctly acting human driver.
机译：这项工作解决了由于感知范围有限而导致场景可观察性有限的情况下在交通场景中进行风险评估的一般问题。在这里，我们特别关注在视觉上难以访问的交叉路口场景。为了区分视线范围，我们使用了公开的数字地图数据，除了一般的道路几何形状外，还包括有关建筑物的信息，这些信息可能会阻碍驾驶员的视野。基于估计的视域，我们使用可能存在但无法感知的关键场景实体来增强感官感知环境。对于那些可能存在的场景实体，我们为自我驱动程序预测最坏情况的行为，并评估即将发生的碰撞风险。然后，可以使用此风险模型来丰富交通场景分析中潜在的即将到来的危险，这些危险是由有限的感官覆盖范围导致的。此外，它还可以用于评估驾驶员当前的行为风险，在驾驶员当前行为被认为是至关重要的情况下向驾驶员发出警告，并就如何以风险平均的方式行事提供建议。通过将结果相交警告系统应用于现实世界场景，我们可以验证我们的方法。所提出的系统的行为与高度正确操作的驾驶员的一般行为高度相似。

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