环境感知系统是智能车辆的重要组成部分，它主要是指依赖于车载传感器对车辆周围环境进行探测。为了保证智能车辆环境感知系统的准确性和稳定性，有必要使用智能车辆车载传感器来检测和跟踪可通行区域的目标。本文提出一种基于激光雷达和摄像机信息融合的目标检测和跟踪算法，采用多传感器信息融合的方式对目标进行检测和跟踪。该算法利用激光雷达点云数据聚类方法检测可通行区域内的物体，并将其投射到图像上，以确定跟踪对象。在确定对象后，该算法利用颜色信息跟踪图像序列中的目标，由于基于图像的目标跟踪算法很容易受到光、阴影、背景干扰的影响，该算法利用激光雷达点云数据在跟踪过程中修正跟踪结果。本文采用 KITTI数据集对算法进行验证和测试，结果显示，本文提出的目标检测和跟踪算法的跟踪目标平均区域重叠为 83.10%，跟踪成功率为 80.57%，与粒子滤波算法相比，平均区域重叠提高了 29.47%，跟踪成功率提高了 19.96%。

As an important part of intelligent vehicle, environmental perception system mainly refers to the detection of the surrounding environment of the vehicle by the sensors attached on the vehicle. In order to ensure the accuracy and stability of the intelligent vehicle environmental perception system, it is necessary to use intelligent vehicle sensors to detect and track objects in the passable area. In this paper, an object detection and tracking algorithm based on the LiDAR and camera information fusion is proposed. The algorithm uses the point cloud data clustering method of LiDAR to detect the objects in the passable area and project them onto the image to determine the tracking objects. After the objects are determined, the algorithm uses color information to track objects in the image sequence. Since the object tracking algorithm based on image is easily affected by light, shadow and background interference, the algorithm uses LiDAR point cloud to modify the tracking results. This paper uses KITTI data set to verify and test this algorithm and experiments show that the target area detection overlap of the proposed target detection and tracking algorithm is 83.10% on average and the tracking success rate is 80.57%. Compared with particle filtering algorithm, the average region overlap increased by 29.47% and the tracking success rate increased by 19.96%.