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Evaluation of automotive forward collision warning and collision avoidance algorithms

机译：汽车前撞预警和避撞算法评估

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Collision warning/collision avoidance (CW/CA) systems target a major crash type and their development is a major thrust of the Intelligent Vehicle Initiative. They are a natural extension of adaptive cruise control systems already available on many car models. Many CW/CA algorithms have recently been proposed but the existing literature mainly focuses on algorithm development. Evaluations of these algorithms have been usually based on subjective ratings. The main contribution of this paper is the utilization of a naturalistic driving data set for the evaluation of CW/CA algorithms. We first collect manual driving data from the ICCFOT project, then process the data by Kalman smoothing, and finally identify 'threatening' and 'safe' data sets according to vehicle brake inputs and vehicle range behavior. Five CW/CA algorithms published in the literature are evaluated against the identified data sets. The performance of these algorithms is determined through a performance metric commonly used in signal detection and information retrieval under unbalanced data population.
机译：碰撞预警/避免碰撞（CW / CA）系统针对主要的碰撞类型，其发展是“智能汽车计划”的主要推动力。它们是自适应巡航控制系统的自然扩展，已经在许多车型上提供。最近已经提出了许多CW / CA算法，但是现有文献主要集中在算法开发上。这些算法的评估通常基于主观评分。本文的主要贡献是利用自然驾驶数据集评估CW / CA算法。我们首先从ICCFOT项目中收集手动驾驶数据，然后通过卡尔曼平滑处理数据，最后根据车辆制动输入和车辆行驶范围识别“威胁”和“安全”数据集。针对确定的数据集评估了文献中发布的五种CW / CA算法。这些算法的性能由性能指标确定，该指标通常用于不平衡数据填充下的信号检测和信息检索。

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