Shi Junren, Tang Yingjie, Gao Jun, Piao Changhao, Wang Zhongquan
School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel). 2023 Aug 3;23(15):6920. doi: 10.3390/s23156920.
Multitarget tracking based on multisensor fusion perception is one of the key technologies to realize the intelligent driving of automobiles and has become a research hotspot in the field of intelligent driving. However, most current autonomous-vehicle target-tracking methods based on the fusion of millimeter-wave radar and lidar information struggle to guarantee accuracy and reliability in the measured data, and cannot effectively solve the multitarget-tracking problem in complex scenes. In view of this, based on the distributed multisensor multitarget tracking (DMMT) system, this paper proposes a multitarget-tracking method for autonomous vehicles that comprehensively considers key technologies such as target tracking, sensor registration, track association, and data fusion based on millimeter-wave radar and lidar. First, a single-sensor multitarget-tracking method suitable for millimeter-wave radar and lidar is proposed to form the respective target tracks; second, the Kalman filter temporal registration method and the residual bias estimation spatial registration method are used to realize the temporal and spatial registration of millimeter-wave radar and lidar data; third, use the sequential m-best method based on the new target density to find the track the correlation of different sensors; and finally, the IF heterogeneous sensor fusion algorithm is used to optimally combine the track information provided by millimeter-wave radar and lidar, and finally form a stable and high-precision global track. In order to verify the proposed method, a multitarget-tracking simulation verification in a high-speed scene is carried out. The results show that the multitarget-tracking method proposed in this paper can realize the track tracking of multiple target vehicles in high-speed driving scenarios. Compared with a single-radar tracker, the position, velocity, size, and direction estimation errors of the track fusion tracker are reduced by 85.5%, 64.6%, 75.3%, and 9.5% respectively, and the average value of GOSPA indicators is reduced by 19.8%; more accurate target state information can be obtained than a single-radar tracker.
基于多传感器融合感知的多目标跟踪是实现汽车智能驾驶的关键技术之一，已成为智能驾驶领域的研究热点。然而，当前大多数基于毫米波雷达和激光雷达信息融合的自动驾驶车辆目标跟踪方法，在测量数据的准确性和可靠性方面存在困难，无法有效解决复杂场景下的多目标跟踪问题。鉴于此，本文基于分布式多传感器多目标跟踪（DMMT）系统，提出了一种适用于自动驾驶车辆的多目标跟踪方法，该方法综合考虑了基于毫米波雷达和激光雷达的目标跟踪、传感器配准、航迹关联和数据融合等关键技术。首先，提出了一种适用于毫米波雷达和激光雷达的单传感器多目标跟踪方法，以形成各自的目标轨迹；其次，利用卡尔曼滤波器时间配准方法和残余偏差估计空间配准方法，实现毫米波雷达和激光雷达数据的时间和空间配准；第三，基于新目标密度使用顺序m-best方法找到不同传感器航迹的相关性；最后，使用IF异构传感器融合算法对毫米波雷达和激光雷达提供的航迹信息进行最优组合，最终形成稳定且高精度的全局航迹。为验证所提方法，在高速场景下进行了多目标跟踪仿真验证。结果表明，本文提出的多目标跟踪方法能够在高速驾驶场景下实现多个目标车辆的轨迹跟踪。与单雷达跟踪器相比，轨迹融合跟踪器的位置、速度、尺寸和方向估计误差分别降低了85.5%、64.6%、75.3%和9.5%，GOSPA指标平均值降低了19.8%；比单雷达跟踪器能获得更准确的目标状态信息。
