The development of the automobile industry and the increase in car ownership has brought great traffic pressures to the city, among which, the difficulty of parking has become a serious problem to the majority of drivers. An automatic parking system can help drivers to complete parking operation or automatic parking task, and a decision control system is an important part of automatic parking system. In this paper, a strategy for generating the shortest parking path based on a bidirectional breadth-first search algorithm combined with a modified Bellman–Ford algorithm is proposed for automatic parking systems. Experimental results show that this scheme can improve the performance of an automatic parking system, especially in a complex environment.

In order to make up for the shortcomings of independent sensors and provide more reliable estimation, a multi-sensor fusion framework for simultaneous localization and mapping is proposed in this paper. Firstly, the light detection and ranging (LiDAR) point cloud is screened in the front-end processing to eliminate abnormal points and improve the positioning and mapping accuracy. Secondly, for the problem of false detection when the LiDAR is surrounded by repeated structures, the intensity value of the laser point cloud is used as the screening condition to screen out robust visual features with high distance confidence, for the purpose of softening. Then, the initial factor, registration factor, inertial measurement units (IMU) factor and loop factor are inserted into the factor graph. A factor graph optimization algorithm based on a Bayesian tree is used for incremental optimization estimation to realize the data fusion. The algorithm was tested in campus and real road environments. The experimental results show that the proposed algorithm can realize state estimation and map construction with high accuracy and strong robustness.

Vehicle yaw stability control is an important part of the active safety of electric vehicles. In order to realize the yaw stability control of vehicles, this paper takes 4-WID electric vehicles as the research object, studies the nonlinear estimation of the state parameters of the lateral stability dynamic system and the yaw stability control strategy. The vehicle state parameter estimation strategy, based on the unscented Kalman filter (UKF) algorithm and the model predictive control algorithm, are designed to control the vehicle yaw stability, which realizes the safe and stable driving of the vehicle. Through CarSim–Simulink joint simulation and hardware-in-the-loop (HIL) experiments based on MicroAutoBox, the effectiveness and real-time performance of the designed control strategy are fully verified, which accelerate the development process of the vehicle controller, and realizes the safe and stable driving of the vehicle.

Aiming at the problem that, under certain extreme conditions, relying on tire force or tire angular velocity to represent the longitudinal velocity of the unmanned vehicle will fail, this paper proposes a longitudinal velocity estimation method that fuses LiDAR and inertial measurement unit (IMU). First, in order to improve the accuracy of LiDAR odometry, IMU information is introduced in the process of eliminating point cloud motion distortion. Then, the statistical characteristic of the system noise is tracked by an adaptive noise estimator, which reduces the model error and suppresses the filtering divergence, thereby improving the robustness and filtering accuracy of the algorithm. Next, in order to further improve the estimation accuracy of longitudinal velocity, time-series analysis is used to predict longitudinal acceleration, which improves the accuracy of the prediction step in the unscented Kalman filter (UKF). Finally, the feasibility of the estimation method is verified by simulation experiments and real-vehicle experiments. In the simulation experiments, medium- and high-velocity conditions are tested. In high-velocity conditions (0–30 m/s), the average error is 1.573 m/s; in the experiment, the average error is 0.113 m/s.

Medical imaging plays a pivotal role in clinical diagnosis. There are many imaging means to detect the state of tissues and organs in the body. However, these imaging methods have their own advantages and shortcomings. For example, magnetic resonance imaging (MRI) has structural information but no functional characteristics of tissue and positron emission tomography (PET) possesses functional characteristics but no structural information. As we all know, it is helpful for clinical diagnosis to integrate the merits of different modality medical image into a single image. This paper proposed an end-to-end multimodal medical image fusion framework, MMI-fuse. We first applied an auto-encoder to extract the features of source images. Then, an Information preservation-weighted Channel-Spatial attention module (ICS) was proposed to fuse the image features. Finally, we used an auto-decoder modal to restructure the fused medical image. To validate the performance of the proposed method, we collected 106 pairs of brain medical images including MRI-PD and MRI-T2, MRI-PD and SPECT-Tc (technetium-99-sestamibi SPECT), Gadolinium-based MRI and SPECT-Tc, Gadolinium-based MRI and SPECT-Tl (thalium-201 SPECT), as well as MRI and Flurodeoxyglucose PET five sets from Harvard dataset for extensive experiments. In addition, seven methods and five metrics were selected for the comparison experiments. The results demonstrates that the proposed method achieved the best performance on both the visual quality and objective metric score among these seven approaches.