JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jera.v10i6.15643ArticleStrategic Analysis of Dynamic Detection and Tracking Methods for Autonomous Vehicleshttps://artdesignp.com/journal/JERA/10/6/10.26689/jera.v10i6.15643KangChenhe20261062026-07-23To solve the problem of perception failure due to vehicle occlusion in urban areas, this paper will review and analyze many dynamic detection and tracking methods proposed in recent years in detail. The main idea of the first representative study is to use laser beams and geometric figures for rigid matching. They are very effective in open areas, but because there is no information on the surface, the algorithm cannot work when there is occlusion. Earlier, researchers put forward a soft-assignment method by building a “four-rectangle measurement model”, and it was found that probabilistic modeling is better than binary matching; now, a laser ray does not need to hit an object, and the matching probability of local sparse point clouds can be calculated. A good way to improve the efficiency of tracking is the scaled sequence algorithm. The first problem of this algorithm is that it involves a high-dimensional state search. The detection rate of severe occlusion in the KITTI dataset for the new model is higher than that of traditional methods. As shown in the above case, although this method is relatively stable in the face of missing values, it has some defects; that is, it is too sensitive to the initial prior and may be trapped in a local optimum under difficult conditions. Due to the shortcomings of the previous models, such as fixed dimensions and an inability to address all kinds of traffic flexibly, it is hoped that future research will be based on multi-scale adaptive geometric models to achieve all-weather sensing and all-conditions operation. Vehicle occlusion, Likelihood Field Model, Scaling sequence algorithm, self-motion compensation.

Geiger A, Lenz P, Stiller C, et al., 2013, Vision Meets Robotics: The KITTI Dataset. The International Journal of Robotics Research, 32(11): 1231–1237.

Chen T, Wang R, Dai B, et al., 2016, Likelihood-Field-Model-Based Dynamic Vehicle Detection and Tracking for Self-Driving. IEEE Transactions on Intelligent Transportation Systems, 17(11): 3142–3158.

Petrovskaya A, Thrun S, 2008, Efficient Techniques for Dynamic Vehicle Detection. Proceedings of the 11th International Symposium on Experimental Robotics: 261–271.

Petrovskaya A, Thrun S, 2009, Model-Based Vehicle Detection and Tracking for Autonomous Urban Driving. Autonomous Robots, 26(2–3): 123–139.

Thrun S, Burgard W, Fox D, 2005, Probabilistic Robotics. MIT Press.

Thrun S, Fox D, Burgard W, 2001, Robust Monte Carlo Localization for Mobile Robots. Artificial Intelligence, 128(1–2): 99–141.

Cellina M, Corno M, Savaresi S, 2025, LiDAR-Based Vehicle Detection and Tracking for Autonomous Racing. arXiv.

Lang A, Vora S, Caesar H, et al., 2019, PointPillars: Fast Encoders for Object Detection from Point Clouds. Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition: 1–8.

Motro M, Ghosh J, 2018, Measurement-Wise Occlusion in Multi-Object Tracking. arXiv.

Petrovskaya A, Khatib O, 2011, Global Localization of Objects via Touch. IEEE Transactions on Robotics, 27(3): 569–585.

Fu C, Dong C, Zhang X, et al., 2019, Low-Cost LIDAR-Based Vehicle Pose Estimation and Tracking. arXiv.

Weng X, Wang J, Held D, et al., 2020, AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics. arXiv.