Multi-target tracking, a high-level vision job in computer vision, is crucial to understanding autonomous driving surroundings. Numerous top-notch multi-object tracking algorithms have evolved in recent years as a result of deep learning’s outstanding performance in the field of visual object tracking. There have been a number of evaluations on individual sub-problems, but none that cover the challenges, datasets, and algorithms associated with visual multi-object tracking in autonomous driving scenarios. In this research, we present an exhaustive study of algorithms in the field of visual multi-object tracking over the last ten years, based on a systematic review approach. The algorithm is broken down into three groups based on its structure: methods for tracking by detection (TBD), joint detection and tracking (JDT), and Transformer-based tracking. The research reveals that the TBD algorithm has a straightforward structure, however the correlation between its individual sub-modules is not very strong. To track multiple objects, the JDT technique combines multi-module joint learning with a deep network framework. Transformer-based algorithms have been explored over the past two years, and they have benefits in numerous assessment indicators, as well as tremendous research potential in the area of multi-object tracking. Theoretical support for algorithmic research in adjacent disciplines is provided by this paper. Additionally, the approach we discuss, which uses merely monocular cameras rather than sophisticated sensor fusion, is anticipated to pave the way for the quick creation of safe and affordable autonomous driving systems.

Accurate control of engine speed can effectively improve fuel economy and comfort. Currently, the commonly used PID parameter setting methods for engine speed control include Ziegler-Nichols method and gradient method, etc. Although they have good performance in parameter setting, they still have shortcomings such as slow response to the control process and long stability time. In this paper, the tuna swarm optimization is used to adjust the PID parameters, and the optimized results are compared with the traditional setting results. The experimental results show that under the same test conditions, the response speed can be reduced by 3.2-6.2s, the maximum overshoot can be reduced by 0.5%-5%, and the maximum steady-state error can be reduced by 0.6%-0.8%.The tuna swarm optimization has an obvious effect in PID parameter optimization of engine speed control, which provides a theoretical basis for the application of other group optimization algorithms in PID parameter optimization of engine speed control.