In order to improve the environmental adaptation and safety of autonomous vehicles trajectory planning process in a complex driving environment, a novel trajectory planning method which meets the requirement of multidriving tasks and adapts to various driving conditions is proposed in this article. In the trajectory planning method, the optimal control problem considering multiple driving tasks is established based on the constructed performance function and constraint analysis of different driving tasks to ensure the accurate realization of driving tasks. Besides, the neural network empirical model, precollision detection model, and trajectory evaluation model are designed by the consideration of selecting the optimal planning parameters in different driving conditions to enhance the adaptability to traffic environment. The advantage of the proposed method is that it not only meets the requirements of a variety of driving tasks, but also able to select the optimal planning parameters according to different traffic conditions while existing methods usually only meet single planning task, such as lane change, and has the fixed and rigid parameter selection. Four different typical scenarios are given to verify the effectiveness of the proposed method and the results show that the proposed trajectory planning method is able to ensure the safety of the vehicle and adapt to different traffic environments flexibly.

In order to prevent the overheating of motors and enhance vehicle stability, a centralized fault prevention control method based on model predictive control that incorporates a motor thermal model is proposed. The motor thermal model is separated into heat generation and heat dissipation components, precisely capturing the impact of motor torque on temperature variations. By dynamically adjusting motor torque distribution, it effectively regulates motor temperature, extends motors lifespan, and addresses the research gap in the coupling of motor thermal management and vehicle motion control. Besides, it coordinates the control of motor torques, steering angles, and active suspension forces within a unified centralized architecture, thereby avoiding response delays and performance conflicts that may arise from distributed control framework, achieving a balance between vehicle dynamics and thermal protection. Finally, the effectiveness of the proposed method in preventing motor overheating and controlling vehicle stability while maintaining ride comfort is validated through hardware-in-the-loop testing.

In this paper, we study the deployment of$K$heterogeneous UAVs to monitor Points of Interest (PoIs) in a disaster zone, where a PoI may represent a school building or an office building, in which people are trapped. A UAV can take images/videos of PoIs and send its collected information back to a nearby rescue station for decision-making. Unlike most existing studies that focused on only homogeneous UAVs, we here study the scheduling of$K$heterogeneous UAVs, where different UAVs have different energy capacities and functionalities that lead to different monitoring qualities (monitoring rewards) of each PoI. For example, one type of UAVs can take only visual images while the other type of UAVs can take both visual and thermal infrared images. In this paper, we investigate a problem of scheduling$K$heterogeneous UAVs to monitor PoIs so that the sum of monitoring rewards received by all UAVs is maximized, subject to energy capacity on each UAV. We propose the very first$\frac {1}{3}$-approximation algorithm for this scheduling problem. We also evaluate the performance of the proposed algorithm, using real parameters of commercial UAVs. Experimental results show that the performance of the proposed algorithm is promising, which is improved by 25%, compared with existing algorithms.

This study is motivated by the maximum connected coverage problem (MCCP), which is to deploy a connected UAV network with given$K$UAVs in the top of a disaster area such that the number of users served by the UAVs is maximized. The deployed UAV network must be connected, since the received data by a UAV from its served users need to be sent to the Internet through relays of other UAVs. Motivated by this application, in this paper we study a more generalized problem – the$h$-hop independently submodular maximization problem, where the MCCP problem is one of its special cases with$h=4$. We propose a$\frac {1-1/e}{2h+3}$-approximation algorithm for the$h$-hop independently submodular maximization problem, where$e$is the base of the natural logarithm. Then, one direct result is a$\frac {1-1/e}{11}$-approximate solution to the MCCP problem with$h=4$, which significantly improves its currently best$\frac {1-1/e}{32}$-approximate solution. We finally evaluate the performance of the proposed algorithm for the MCCP problem in the application of deploying UAV networks, and experimental results show that the number of users served by deployed UAVs delivered by the proposed algorithm is up to 12.5% larger than those by existing algorithms.