Intelligent vehicles should be able to detect various obstacles and also identify their types so that the vehicles can take an appropriate level of protection and intervention. This article presents a method of detecting and classifying multiclass obstacles for intelligent vehicles. A stereovision-based method is used to segment obstacles from traffic background and measure three-dimensional geometrical features. A Bayesian network (BN) model has been established to further classify them into five classes, including pedestrian, cyclist, car, van, and truck. The BN model is trained using substantial data samples. The optimized structure of the model is determined from the necessary path condition method with a presupposition constraint (NPC+PC). The conditional probability table of the discrete nodes and the conditional probability distribution of the continuous nodes are determined from expectation maximization (EM) training algorithm with consideration of prior domain knowledge. Experiments were conducted using the object detection data set on the public KITTI benchmark, and the results show that the proposed BN model exhibits an excellent performance for obstacle classification while the full pipeline of the method including detection and classification is in the upper middle level compared with other existing methods.

中文翻译：

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基于立体视觉和贝叶斯网络的智能车辆多类障碍物检测和分类

智能车辆应该能够检测各种障碍物并识别它们的类型，以便车辆可以采取适当级别的保护和干预。本文提出了一种检测和分类智能车辆多类障碍物的方法。基于立体视觉的方法用于从交通背景中分割障碍物并测量三维几何特征。建立了贝叶斯网络（BN）模型，将它们进一步分为五类，包括行人、自行车、汽车、面包车和卡车。BN 模型使用大量数据样本进行训练。模型的优化结构由带预设约束的必要路径条件法（NPC+PC）确定。离散节点的条件概率表和连续节点的条件概率分布是通过考虑先验领域知识的期望最大化(EM)训练算法确定的。使用公共 KITTI 基准上的对象检测数据集进行了实验，结果表明，所提出的 BN 模型在障碍物分类方面表现出优异的性能，而包括检测和分类在内的方法的完整流程与其他现有方法。