This paper systematically investigates the prediction of Vulnerable Road Users’ (VRU, including pedestrian, cyclist, and electric cyclist) trajectories by leveraging the Action Intention Model (AIM), Revised Social Force Model (RSFM) and Social Attention-Transformer-Long Short Term Memory Network (SA-TF-LSTM) for automated vehicles. Firstly, an AIM based on the Transformer is developed for predicting the VRUs’ crossing/waiting intention. VRU type and heterogeneity (age and gender), distance between vehicle and VRU, speed of vehicle and VRU are considered. Secondly, a micro-dynamic RSFM is used to model the trajectories of VRUs for generating initially hypothetical future trajectories, which are then merged the historical trajectories with observed time as a new feature input. Furthermore, traffic data gathered by an unmanned aerial vehicle (UAV) is acquired and examined, and the Maximum Likelihood Estimation (MLE) is utilized to adjust the parameters of the RSFM. Finally, a data driven SA-TF-LSTM is proposed for VRU trajectory prediction, and VRU crossing intention, traffic-actor interaction, VRU type and heterogeneity are considered. Social Attention is employed to ascertain the attention coefficients of the aforementioned factors. The results demonstrate that the data driven SA-TF-LSTM surpasses the existing methods, with a prediction accuracy enhancement of over 9% utilizing the collected traffic data. This significant improvement grants us substantial confidence in employing the SA-TF-LSTM within automated vehicles to bolster the safety of VRUs.

This paper presents a systematic investigation into path planning and trajectory tracking for autonomous vehicles. By integrating an improved A* algorithm, a fuzzy dynamic window approach, and a Fuzzy PID control strategy, the proposed method enables effective driving of an autonomous vehicle. Firstly, in the global path planning phase, to address the issues of low computational efficiency and suboptimal path quality in traditional A* algorithms for large-scale map searches, an improved A* algorithm incorporating an enhanced heuristic function, redundant node removal strategy, and path smoothing approach is introduced, significantly increasing search efficiency and optimizing path quality. Secondly, in the local path planning phase, the dynamic adjustment of vehicle speed and steering is achieved by combining fuzzy logic control with the dynamic window approach. This allows for smooth obstacle avoidance in dynamic environments. Furthermore, a path smoothing algorithm is integrated to refine the generated trajectory, ensuring its continuity and smoothness. Finally, a Fuzzy PID control algorithm is integrated into the trajectory tracking controller. By introducing fuzzy logic, the PID parameters are adaptively adjusted to ensure precise vehicle following of the planned path, improving path tracking stability and response speed. The proposed method is validated and evaluated in a variety of complex road scenarios using a real vehicle based on ROS. The simulation and real-world experimental results clearly illustrate that the proposed method achieves substantially better performance than conventional approaches with regard to path planning efficiency, obstacle avoidance success rate, and path smoothness.

Safety has become a critical issue in the development of automated driving systems (ADSs). Drivers' perception of risk determines their acceptance, trust, and use of ADS. However, driver's perceived risk is subjective and difficult to evaluate using traditional risk assessment methods. To address this issue, a driver's subjective perceived risk (DSPR) model is proposed, which regards driver's perceived risk as a dynamically triggered mechanism that exhibits anisotropy and attenuation. Subsequently, 20 participants are recruited for a driver-in-the-loop experiment to report their real-time subjective risk ratings (SRRs) when experiencing various real-world automated driving scenarios. A convolutional neural network and bidirectional long short-term memory with temporal pattern attention (CNN-Bi-LSTM-TPA) network is embedded into a semisupervised learning strategy to predict driver's SRRs, aiming to reduce data noise caused by the subjective randomness of drivers. Results illustrate that our proposed DSPR model combined with the semisupervised learning strategy achieves the highest prediction accuracy of 87.91% in predicting driver's SRRs, outperforming other three state-of-the-art risk models. Compared to the model trained on the original data, the semisupervised method improves accuracy by 20.12%. The proposed CNN-Bi-LSTM-TPA network presents the highest among four different network structures. Finally, a genetic algorithm is applied to optimize parameters, which improves the accuracy to 89.95%. This study offers an effective method for assessing driver's perceived risk, providing support for the safety enhancement of ADS and driver's trust improvement.

We proposed an unsupervised pipeline AW-FRVP for denoising of point clouds in adverse weather to alleviate the noise points’ impact on perception. Specifically speaking, AW-FRVP consists of two stages: firstly, AWDenoiseNet was adopted to gain efficient pseudo labels by converting the point cloud into frequency domain with depth and intensity features. Secondly, the semantic segmentation network FRVPNet was trained based on the generated pseudo labels and tested for binary classification of noise and environmental points. In the first stage, the KNN-FLATTEN and Mamba-RFE feature extractor were equipped to alleviate the many-to-one issue while converting the point cloud to a range perspective and strengthening the key feature extraction in the frequency domain. In the second stage, point features corresponding to different perspectives were transformed and fused, and two modules named FPFusion-with-CSMA, as well as Triple-Points-Fusion-head, were adopted to gain point-wise features with rich information for subsequent binary classification. Amount of experiments show that the AWDenoiseNet can gain real-time and efficient point cloud denoising with precision of 93.47% on WADS and recall of 94.70% on Weather-KITTI (Rain Scenes). Without annotation, the AWDenoiseNet can gain the SOTA performance among the unsupervised denoising methods. What’s more, the training process of FRVPNet based on pseudo labels generated by AWDenoiseNet can gain sound denoising performance. Both AWDenoiseNet and FRVPNet can achieve real-time denoising with frequencies of about 165 FPS (Frame Per Second) and 19 FPS. In summary, our two-stage unsupervised denoising pipeline can achieve real-time SOTA label-free denoising with a slight gap to those fully-supervised methods (3.65% in precision on WADS, and 5.71% in recall on rain scenes of Weather-KITTI) and our method can significantly reduce the interference of noise in point clouds on perception algorithms for internet of traffic participants.

Accurate prediction of vehicle motions is imperative for enabling cooperative perception and planning of autonomous vehicles, however effective modeling of complex spatio-temporal interactions and long-term dependencies between vehicles remains a formidable challenge. To tackle these issues, we propose a novel motion query-based social transformer network (MOST) for vehicle trajectory and intention prediction through a multi-task approach, which is composed of temporal transformer encoder module, social interaction module and motion queries-based multi-task feature decoder in a hierarchical manner. The temporal transformer is responsible for capturing long-range temporal correlations of individual motion states through a self-attention mechanism with residual connection, while the spatial interaction dependencies between vehicles are acquired through the social interaction module by constructing social tensors. Furthermore, Considering the uncertainty and diversity of future vehicle behaviors, a motion query-based feature decoder is proposed, which is equipped with learnable parameters to assimilate prior knowledge and generate multiple possible future trajectories and intentions. To assess our model’s effectiveness, we carried out comprehensive experiments on the open-source NGSIM and HighD dataset. The results demonstrated that our approach reaches unparalleled performance, with an average prediction accuracy improvement of about 50% on the NGSIM dataset and 20% on the HighD dataset compared to the state-of-the-art method.

Accurately predicting the actions of vulnerable road users (VRUs) is crucial for improving traffic flow and enhancing VRU safety. The unpredictable nature of VRU trajectories poses a significant challenge. To address this, we introduce the Probabilistic Multimodal Trajectory Prediction Network (PMTPN), which effectively forecasts multimodal trajectories and their corresponding probabilities by utilizing a multitask learning framework that integrates trajectory and probability predictions. The network processes diverse input modalities, including bounding boxes, pedestrian pose, and ego-vehicle motion information. We enhance prediction performance by employing specialized encoders to extract distinct features from these inputs and a fusion module to integrate the data efficiently. To manage the variability in pedestrian actions, our model incorporates learnable motion queries that serve as reference points for predicting various potential outcomes. These queries are iteratively refined through attention operations with historical context in a multi-layer decoder. Additionally, a multi-gate mixture-of-experts (MMoE) module within the decoder helps mitigate the challenges of multitask learning. Our method significantly enhances trajectory prediction accuracy and provides probabilities for each predicted trajectory, demonstrating state-of-the-art results on the JAAD and PIE datasets.

The security of cyber-physical systems (CPS) constitutes a crucial aspect in system design and control. This paper investigates the cyber security of an electric vehicle under a malicious cyber attack from a control design perspective. The work presented in this study focuses on addressing the problem of encountering a certain type of malicious attacks, false data injection (FDI), that can damage the system and prevent the actuators from working properly. First, the problem is formulated and a convergence analysis is carried out, then a risk-averse controller is derived for the unconstrained and constrained control input signal. Next, a statistical-based algorithm, using the z-score and cumulative sum, was devised to detect possible attacks that may affect the system. Computer-aided simulations were performed using a vehicle model for the regulation and tracking problems. The results have proven the efficacy of the proposed method to detect and counteract a specific type of cyber attacks.

Obtaining high-resolution battery historical data in the cloud is crucial for lithium-ion battery state estimation and thermal runaway prediction. Due to limited signal transmission bandwidth, the cloud primarily stores low-frequency (LF) and less high-frequency (HF) data. This article proposes a model-based data compression and recovery method to efficiently transfer battery signals between electric vehicles and the cloud. First, training datasets are generated from real vehicle data. Then, a multitask learning model, within a semisupervised framework, is presented to learn the HF voltage representation of each cell. The semisupervised learning task utilizes unlabeled LF voltages to enhance the voltage recovery effects. Multitask learning is employed to address the issue of target domain drifting caused by cell aging. Finally, battery data from vehicle and laboratory tests are utilized to compare the results of different methods on voltage recovery tasks. The results demonstrate that the proposed method can reduce the average voltage recovery error to less than 8 mV with a compression ratio of 10.

In this paper, the mixed traffic scenario with human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs) on freeway is considered. In this partly known nonlinear system, an optimal control algorithm using adaptive dynamic programming (ADP) is proposed to deal with the communication latence and drivers’ reaction time, which can stabilize the system under the influence of dead zone and saturation with minimal cost. There are three contributions in this paper. Firstly, in the used ADP algorithm, a critic neural network (NN) is designed to estimate the optimal value of the cost function, which is updated using online data instead of pre-gathered data. This means that the proposed controller can adapt to different parameters of different systems. Secondly, the reaction time of human driver and the time latence of the V2V communication are considered as the state and input delay of the nonlinear system, by adding the terms of delayed states to the optimal value function, the influence of the time delay can be minimized in the process of the critic NN updating. Thirdly, the saturation and dead zone of actuator are considered, by designing a new utility function of control value, the control value is limited from being out of the expected range. Under this condition, the stabilization of the overall system and the effectiveness of the proposed algorithm is proved and validated by means of simulation results.

Passenger comfort is a crucial aspect that influences humans’ acceptance of automated vehicles. The passenger comfort score (PCS) is closely related to the passengers’ psychological states, however, comfort quantification methods based on the passengers’ psychophysics mechanism are rare. This research pioneers a passenger comfort quantification model (PCQM) specifically designed for automated vehicles, demonstrating the model’s ability to accurately quantify subjective PCS under urban LCS. Three significant contributions form the basis of this study: 1) A dataset dedicated to comfort quantification is collected. A novel PCQM based on ensemble learning of psychophysics mechanism based sub-model and encoder-transformer based sub-model is proposed. The psychophysics mechanism model is derived from Stevens’ power law. 2) As a subjective indicator, the self-reported score (SRS), which is the indicator of PCS contains considerable noise. The PCQM addresses the issue of substantial noise prevalent in the subjective SRS by incorporating a semi-supervised learning strategy, which enhances data consistency and suppresses noise. 3) The efficacy of the proposed PCQM is corroborated via deployment on an automated vehicle, where the model’s real-time predictions strongly align with SRS from onboard passengers.

In this paper, Vulnerable Road User (VRU) trajectory prediction for autonomous driving based on the Intention-Attention-Gate Recurrent Unit (IA-GRU), Improved Social Force Model (ISFM) and Adaptive Boosting (AdaBoost) is systematically investigated. Firstly, a novel IA-GRU is proposed for VRU (pedestrian, cyclist, and electric cyclist) trajectory prediction. VRU intention (waiting/crossing), VRU heterogeneity (age and gender), VRU-VRU interactions and VRU-dynamic vehicle interactions are taken into account. Attention is used to obtain the influence weights of the above factors used for VRU trajectory prediction. Secondly, a micro-dynamic ISFM is developed for VRU trajectory prediction. The impact of zebra crossing, collision avoidance with vehicles and VRUs, and VRU heterogeneity are considered. Moreover, traffic data collected by an unmanned aerial vehicle (UAV) is obtained and analyzed, and the parameters of the ISFM are calibrated by the Maximum Likelihood Estimation (MLE). Finally, a data-driven integrated approach based on the IA-GRU and ISFM is proposed, and AdaBoost is used to prevent the model from overfitting and improve the prediction accuracy. The results indicate that the integrated model outperforms the existing methods, and the prediction accuracy is improved by more than 11% based on the collected traffic data, which can give us great confidence to use the integrated model in the autonomous driving domain to improve the safety of VRUs.

With the rise of the electric vehicle market, wireless charging technology for electric vehicles as a convenient, fast and safe new charging technology has been focused on research in recent years. Compared with traditional wired charging, wireless charging has great advantages in terms of economy, convenience, safety, and adaptability. To improve the performance of inductive power transfer (IPT) systems, it is essential to pursue an optimized design of the magnetic couplers. Although the result of the Traditional finite element method (FEM) is very accurate, it needs a lot of time. Hence, for the first time, this paper proposes a general data-driven coil design approach based on the combination of a neural network and the multi-objective optimization algorithm. The proposed method significantly accelerates the design process and provides highly compliant design results. A design example of an integrated magnetic coupler is provided to validate the superiority of the proposed method in time-saving and accuracy, where the FEM simulation results show that the design deviation is within 10%.

There has been a tendency to embed the theoretical thermal model of electric machines into vehicle thermal management systems to realize real-time temperature prediction and advanced cooling adjustment. The construction process of the high-precision analytical model is the most difficult part for engineering application. However, the electric machines used in electric vehicles usually work in a wide operation space, in which the alternating electromagnetic fields produce different heat production behavior in different operating states. Meanwhile, the major loss components and the main heat transfer paths are highly affected by the temperature variation. The traditional modeling methods usually ignore these effects and oversimplify the model, which will lead to obvious deviations. The main contribution of this article is to propose a theoretical modeling approach with high predicting accuracy in the whole operation space of the electric machines. The experimentally test shows the real-time predicting accuracy has been improved by nearly 60% in a standard test cycle. The computation cost when using the proposed thermal model in the thermal management system is also evaluated to ensure engineering application.

Pedestrian trajectory prediction is essential for collision avoidance in autonomous driving, which can help autonomous vehicles have a better understanding of traffic environment and perform tasks such as risk assessment in advance. In this paper, pedestrian path prediction at a time horizon of 2s for autonomous driving is systematically investigated using waiting/crossing decision model (W/CDM) and modified social force model (MSFM), and the possible conflict between pedestrians and straight-going vehicles at an un-signalized crosswalk is focused on. First of all, a W/CDM is efficiently developed to judge pedestrians' waiting/crossing intentions when a straight-going vehicle is approaching. Then the humanoid micro-dynamic MSFM of pedestrians who have been judged to cross is characterized by taking into account the evasion with conflicting pedestrians, the collision avoidance with straight-going vehicles, and the reaction to crosswalk boundary. The influence of pedestrian heterogeneous characteristics is considered for the first time. Moreover, aerial video data of pedestrians and vehicles at an un-signalized crosswalk is collected and analyzed for model calibration. Maximum likelihood estimation (MLE) is proposed to calibrate the non-measurable parameters of the proposed models. Finally, the model validation is conducted with two cases by comparing with the existing methods. The result reveals that the integrated method (W/CDM-MSFM) outperforms the existing methods and accurately predicts the path of pedestrians, which can give us great confidence to use the current method to predict the path of pedestrian for autonomous driving with significant accuracy and highly improve pedestrian safety.

Battery fast charging strategies have gained an increasing interest toward the convenience of battery applications but may unduly degrade or damage the batteries. To harness these competing objectives, including safety, lifespan, and charging time, in this article, we propose a novel health-aware multiobjective optimal charging strategy to simultaneously shorten the charging time and relieve the battery degradation. The multiobjective optimal charging problem is formulated based on a coupled electrochemical-thermal-aging battery model. Constraints are explicitly imposed on physically meaningful state variables to avoid hazardous operations. Charging duration and battery aging process are well traded-off. Strategies for minimum-time and health-aware fast charging are investigated using different input current bounds, subject to both side reaction and temperature constraints. The experimental results validate that the presented multiobjective health-aware optimal charging algorithm is capable of reducing the charging time from its benchmarks largely without sacrificing the state-of-health of the battery.