The prediction of lane-change behavior is a challenging issue in intelligent and connected vehicles (ICVs), which can help vehicles predict in advance and change lanes safely. In this paper, a novel intelligent approach, which considering both the driving style-based lane-change environment and the driving trajectory-related parameters of the ICV and surrounding vehicles, is proposed to predict the lane-change behaviors for ICVs. By analyzing the characteristics of the lane-change behavior of the vehicle, a modified dataset for the prediction of lane-change behavior was established based on the Next-Generation Simulation (NGSIM) dataset, which is made up of real vehicle trajectories collected by camera. In the proposed approach, the hidden Markov model (HMM)-based model is designed to judge whether the current environment is suitable for lane change according to the driving environment parameters around the vehicle; then according to the driving state of the vehicle, a learning-based prediction-then-judgment model is proposed and designed to realize the prediction of the ICV’s lane-change behavior. Experiments are implemented by using the modified dataset. From the experimental results, the lane-change probability value on the target lane in the truth of the lane-change behavior calculated by the designed HMM-based model is basically above 0.5, indicating that the model can make a more accurate judgment on the surrounding lane-change environment. The proposed learning-based prediction-then-judgment model has an accuracy of 99.32% for the prediction of lane-change behavior, and the accuracy of the lane-change detection algorithm in the model is 99.56%. The experimental results show that the proposed approach has a good performance in the prediction of lane-change behavior, which could effectively assist ICVs to change lanes safely.

Vehicle taillight intention detection is an important application for perception and decision making by intelligent vehicles. However, effectively improving detection precision with sufficient real-time performance is a critical issue in practical applications. In this study, a vision-based improved lightweight approach focusing on small object detection with a multi-scale strategy is proposed to achieve application-oriented real-time vehicle taillight intention detection. The proposed real-time detection model is designed based on YOLOv4-tiny, and a spatial pyramid pooling fast (SPPF) module is employed to enrich the output layer features. An additional detection scale is added to expand the receptive field corresponding to small objects. Meanwhile, a path aggregation network (PANet) is used to improve the feature resolution of small objects by constructing a feature pyramid with connections between feature layers. An expanded dataset based on the BDD100K dataset is established to verify the performance of the proposed method. Experimental results on the expanded dataset reveal that the proposed method can increase the average precision (AP) of vehicle, brake, left-turn, and right-turn signals by 1.81, 15.16, 40.04, and 41.53%, respectively. The mean average precision (mAP) can be improved by 24.63% (from 62.20% to 86.83%) at over 70 frames per second (FPS), proving that the proposed method can effectively improve detection precision with good real-time performance.

V2X technology enables the intelligent and connected vehicles (ICVs) to communicate with surrounding vehicles in real time. In order to obtain the data related to the lane-change behavior of the ICVs and its surrounding vehicles, a positioning data acquisition system with high precision is designed, including hardware and software. In the designed system, the high-precision position, velocity, acceleration and direction angle data are acquired based on the Sinan navigation high-precision receiver M100, the MK5 OBU of Cohda Wireless can be used to realize real-time communication of information, and the main controller is designed based on STM32, completes the collection and transmission of high-precision positioning information, and stores it in the SD card after time synchronization. The relevant data was collected by installing the designed system on the test vehicle, and the lane-changing behavior was manually marked and time stamped. The analysis of experimental results shows that the designed system can basically achieve lane-level accuracy in urban open road scenes, and accurately describe the trajectory of vehicle lane-change behavior.

Microelectromechanical systems (MEMS) inertial measurement unit (IMUs) provide three-dimensional angular velocity and acceleration sequence signals, commonly used in attitude estimation and navigation applications. However, raw MEMS IMU signals often exhibit nonlinear errors that accumulate over time, compromising overall system accuracy. This paper proposes a data-driven denoising method designed to estimate and compensate for the nonlinear errors inherent in MEMS IMU signals. The approach leverages a temporal convolutional network (TCN)-based model to learn error features from ground-truth data on attitude and velocity. The trained model utilizes MEMS IMU sequence signals to estimate IMU errors during testinging, thereby reducing integration errors in orientation, velocity, and position. Validation is performed using a public integrated navigation dataset containing various grades of MEMS IMUs. Experimental results demonstrate that the denoised consumer-grade MEMS IMU signals achieve industrial-level accuracy in attitude, velocity, and position estimation. This confirms the method's effectiveness in compensating for errors in MEMS IMU signal sequences.

The detection of traffic signs through vehicle camera sensors is a crucial task for intelligent vehicles. Prohibition sign is one of the traffic signs that have a significant impact on traffic safety. However, previous research has rarely specifically focused on prohibition signs, and the accurate and rapid detection of prohibition signs remains a challenge. In this paper, a dataset with 12 most common types of Chinese prohibition signs is established, and an improved lightweight model specifically for prohibition sign detection is proposed based on YOLOv5n. In the early stage of the feature extraction network, Shuffle Attention (SA) is introduced to capture detailed features of the target. In the feature fusion network, a multi-path weighted fusion approach is employed to fully utilize valuable information from feature maps at different scales. In the detection output network, to prevent adverse effects on detection performance from other scales, only the 80x80 detection head scale is retained. Experimental results on the established dataset show that, compared to the original model, the detection accuracy mAP@0.5 of the improved model is increased by 20.4%, with a simultaneous 31.7% reduction in the parameter count. Compared with other lightweight models such as Faster RCNN, SSD, and YOLO series, the proposed model also has higher detection accuracy and fewer model parameters. Additionally, the model is deployed on Jetson TX2 and Raspberry Pi 4B with detection speeds of 21.3 and 1.8 FPS respectively, showcasing its potential for practical applications of vehicle camera sensors. The code of the proposed model is shared online at https://github.com/Tomming-Tong/yolov5_ms8.

In the context of increasingly complex and diverse traffic environments, where intelligent and connected vehicles (ICVs) coexist with conventional human‐driven vehicles, maintaining reliable navigation under global navigation satellite system (GNSS) outages is crucial for supporting adaptive driving strategies and ensuring operational safety. Low‐cost inertial measurement units (IMUs) offer promising solutions due to their low computational load and high self‐sufficiency, yet error accumulation remains a persistent challenge, particularly in real‐world mixed traffic scenarios. This study introduces a dilated convolutional neural network (DCN)–driven framework to directly estimate vehicle forward velocity and IMU error parameters from raw IMU measurements, addressing the reliance on hardware‐based odometry by extending nonholonomic constraints (NHC) into three‐dimensional velocity constraints. By dynamically optimizing IMU error parameters through integration with an error model, the proposed method mitigates the adverse effects of inherent noise in low‐cost IMUs, enabling robust navigation in GNSS‐denied environments. Validation using a GNSS/INS dataset demonstrates that the approach accurately estimates vehicle position while significantly suppressing error accumulation, which is pivotal for maintaining reliable navigation in heterogeneous traffic flows where autonomous and human‐driven vehicles coexist. This contributes to the development of robust vehicle autonomy and enhanced safety in mixed‐traffic ecosystems, enabling more adaptive and resilient driving strategies.