The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots.

Although autonomous vehicles leverage perceptual information to execute downstream tasks such as prediction and control to enhance driving safety, they are vulnerable to cyberattacks. Attackers can manipulate model outputs by tampering with inputs. This study proposes a Counterfactual Generation Trend Attack (CGTA) framework for predicting rear-end collision risk. It manipulates inputs within the Key Feature Region using an optimization algorithm, aiming to either elevate (increase attack) or degrade (decrease attack) the predicted driving safety. Furthermore, considering scenarios involving partial knowledge stealing where model parameters are inaccessible, an attack scheme based on model distillation is proposed. After processing trajectory data and quantifying rear-end collision risk, experiments were conducted on three mainstream prediction models. The results demonstrate that: (1) CGTA can effectively execute real time targeted attacks. Simultaneously, the resistance to attack varies under attack directions and models. The deviation of model outputs from ground truth was from 2.87% to 54.75% under increased attacks and from −3.29% to −86.62% under decreased attacks; (2) the multi-head attention (MHA) exhibited superior attack resistance. The attack effects of the prediction models reduced by 41.69% to 86.10% after incorporating MHA; and (3) although the attack effectiveness under partial knowledge stealing decreased, it realized the target attack trend. In addition, this study provides a detailed sensitivity analysis of the parameters and verifies the generalization of the CGTA. The findings reveal the feasibility and effect mechanisms of targeted cyberattacks on autonomous driving systems, while offering theoretical support for defense against such attacks.

The mixture of autonomous vehicles (AVs) and human-driven vehicles (HVs) dominates the current road traffic. AVs can record their own motion and measure the trajectories of surrounding HVs, and these recorded trajectories support mixed-driving research. Due to differences in measurement processes and complex traffic environments, HV trajectories generally exhibit stronger local perturbations than AV trajectories. However, existing mixed-driving research rarely accounts for this difference in local disturbances, which may affect subsequent analyses. This study proposes a local disturbance alignment (LDA) method to align local perturbations in HV and AV trajectories by optimizing filtering parameters. The effectiveness of LDA is validated through car-following modeling and risk prediction on Argoverse 2 and waymo open motion dataset (WOMD). Meanwhile, the differences among car-following pairs are revealed through the proposed resilience measurement framework. The results show that: (1) The differences in local disturbances between HV and AV trajectories are widespread in mixed-driving trajectory datasets processed by various filtering approaches; (2) The proposed LDA substantially reduces errors in car-following modeling and risk prediction compared with the classical filtering approach; (3) AVs exhibit a favorable car-following pattern characterized by assertive acceleration and gentle deceleration, and they achieve the highest and most stable car-following resilience scores. This study provides a novel perspective on mixed-driving trajectory processing and safety evaluation, and the processed WOMD car-following samples are publicly released ( https://github.com/HuansongZhang/Waymo-Open-Motion-Data-Car-following ).

Lane-changing is a typical scenario of driver interaction, which causes a multitude of traffic crashes as well. Therefore, clarifying the lane-changing interaction process is of great significance for driving safety. Although extensive research has been carried out in this respect over the past decades, there are still unsolved challenges ranging from data collection to model construction. Moreover, the influence of human factors on the interactive decision-making and behavior is not fully taken into account. In this study, we start with behavioral data collection based on a two-vehicle simulated driving experiment, followed by the construction of a recurrent multi-time step game modeling framework, in which driving styles of both drivers are considered. More specifically, we design 8 lane-changing experimental scenarios at urban expressways, and conduct a two-vehicle simulated driving experiment and combine it with a driving style questionnaire (MDSI) to collect the interactive driving behavior data and driving style information of 40 experimenters, based on which we analyze the influence of different factors on these drivers' lane-changing interactive behavior. Thereafter, we select the significant influencing factors to construct a payoff function and propose a dynamic game model that can explain the mechanism of the interactive decision from a microscopic perspective. Compared with previous lane-changing game models, the proposed model incorporates driver styles into the game payoff function and establishes a recurrent multi-time step interactive decision-making framework by a dynamic game tree to capture the interdependence of drivers' interactive decisions. The model is solved by parallel computing methods. Comparing the vehicle trajectory prediction results calculated by different models, we find that the error of the multi-time step model considering driver styles is 57.94% lower than that of the single-time step model without driver styles, which implies that the model proposed in this study can better simulate the process of drivers' actual lane-changing interaction behavior by considering the drivers' prediction planning cycle and driving styles. Especially, the driver styles have a more significant effect on reducing the model errors (42.87%), which however, was often ignored in the existing studies.