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.

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.

Pedestrian trajectory prediction plays a critical role in ensuring the safe operation of autonomous vehicles. Predicting from an egocentric view can eliminate the cumulative computational errors associated with scene perspective transformations. However, compared to predictions from a bird’s-eye view, a key challenge in the egocentric setting is that both the ego vehicle’s motion and the pedestrian’s motion simultaneously influence the target’s movement. To address this, we propose an agent-wise motion fusion network (AANet), which efficiently predicts the multimodal trajectories by learning the agent-wise motion step by step, and history trajectories feature in a two-stream structure. Specifically, we utilize the trajectory of the pedestrian, the ego vehicle and pedestrian motion to predict the multimodal trajectory of the pedestrian. One stream of the AANet studies the contextual information by the step-wise attention of the agent-wise motion to enhance the scenario understanding, while the other stream studies the temporal relationship of the trajectory. In addition, a query-based multistage decoder is designed and the prediction of the crossing intention of the pedestrian serves as an auxiliary task, which helps to understand the high-level motivation of the future motion of the pedestrian. Finally, the prediction results on the joint attention for autonomous driving (JAAD) and pedestrian intention estimation (PIE) datasets improve approximately 13% and 12%, respectively, demonstrating the effectiveness and our model achieves state-of-the-art performance.

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.

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.

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.