In the recent literature, on the one hand, many 3D multi-object tracking (MOT) works have focused on tracking accuracy and neglected computation speed, commonly by designing rather complex cost functions and feature extractors. On the other hand, some methods have focused too much on computation speed at the expense of tracking accuracy. In view of these issues, this paper proposes a robust and fast camera-LiDAR fusion-based MOT method that achieves a good trade-off between accuracy and speed. Relying on the characteristics of camera and LiDAR sensors, an effective deep association mechanism is designed and embedded in the proposed MOT method. This association mechanism realizes tracking of an object in a 2D domain when the object is far away and only detected by the camera, and updating of the 2D trajectory with 3D information obtained when the object appears in the LiDAR field of view to achieve a smooth fusion of 2D and 3D trajectories. Extensive experiments based on the typical datasets indicate that our proposed method presents obvious advantages over the state-of-the-art MOT methods in terms of both tracking accuracy and processing speed. Our code is made publicly available for the benefit of the community.

Adaptive cruise control (ACC) systems are designed to provide longitudinal assistance for drivers to enhance safety and reduce workload. As the core of all ACC control algorithms, the spacing policy plays a crucial role in various aspects. This paper presents a comprehensive survey on spacing policies for existing ACC solutions in the literature. The objectives of this paper are to clarify the operating mechanisms and characteristics of the common spacing policies, and to reveal their advantages and shortcomings by means of a comparative study. In this survey, the general evaluation criteria for spacing policies are first introduced. Then, the existing spacing policies are categorized into different types according to their operating mechanisms, and their characteristics are carefully reviewed and explained. A comparative study is followed to analyze the performances of five typical spacing policies in the literature, including the constant spacing policy, constant time headway, traffic flow stability, constant safety factor and human driving behavior spacing policies. The contents provided in this paper serve as a tool for understanding current ACC spacing policies, and pave the way for future ACC enhancement.

A secondary loop cooling battery thermal management system is designed, and then, a phased control strategy for adjusting the compressor speed according to the battery temperature interval is proposed. On this basis, the compressor speed as the decision variable, and the energy consumption of the compressor and the aging losses of the battery are as the optimization goals, which constitute a multi-objective optimization model, and a genetic algorithm is adopted to solve it. Under different weight coefficients, the Pareto Frontier of the energy consumption of the compressor and the aging losses of the battery are established. The simulation analysis is conducted on high speed dynamic conditions at an ambient temperature of 30 °C. The effects of coolant flow rate and compressor speed on battery pack temperature rise and temperature uniformity are analyzed. The simulation results show that the energy consumption of the phased control strategy is reduced by 10.7% compared with the traditional constant compressor speed control strategy under the same conditions. Under different weight coefficients, different simulation results and control strategies can be obtained, and results show that the maximum temperature and temperature uniformity can meet the requirements. There is a contradiction between the energy consumption of compressor and the aging losses of battery, but both them are highly sensitive. According to the Pareto Frontier curve, when the weight coefficient is 0.17, a balanced control strategy can be obtained, which can reduce the battery aging losses of 61.8% by only sacrificing 9.22% of the vehicle driving mileage.

This paper proposes a multi-object tracking (MOT) method called StrongFusionMOT which fuses the information of LiDAR and camera sensors. The major contributions of the proposed StrongFusionMOT are in three aspects. Firstly, in the detection fusion stage, the depth information extracted by means of AD-census is supplemented to 2D detections to facilitate fusion of 2D and 3D detections. This detection fusion pattern enhances fusion robustness and provides accurate fusion performance. Secondly, a new cost function design named SDIoU is proposed by taking into account not only the intersection between the two bounding boxes, but also their shapes and relative distances. This cost function eliminates the shortcomings of the existing IoU designs and greatly enhances association precision. Thirdly, a multi-frame matching mechanism that involves tracks in the past n frames is proposed for reappeared tracks, which effectively suppresses cumulative errors resulting from consecutive frames of track predictions and greatly enhances association robustness. The effectiveness of the proposed StrongFusionMOT is evaluated by means of comparative experiments with state-of-the-art MOT solutions using the KITTI dataset and the nuScenes dataset. Both quantitative and qualitative results demonstrate the superiorities of the proposed method in terms of various performance metrics.

Accurate estimation of battery state of charge (SOC) is of great significance for extending battery life, improving battery utilization, and ensuring battery safety. Aiming to improve the accuracy of SOC estimation, in this paper, a temperature-dependent second-order RC equivalent circuit model is established for lithium-ion batteries, based on the battery electrical characteristics at different ambient temperatures. Then, a dual Kalman filter algorithm is proposed to estimate the battery SOC, using the proposed equivalent circuit model. The SOC estimation results are compared with the SOC value obtained from experiments, and the estimation errors under different temperature conditions are found to be within ±0.4%. These results prove that the proposed SOC estimation algorithm, based on a temperature-dependent second-order RC equivalent circuit model, provides accurate SOC estimation performance with high temperature adaptability and robustness.