The decision behavior of the leading vehicle driver significantly impacts the safety of the connected mixed platoon, especially on the longitudinal decision during emergency braking events. However, driving abilities vary among leading vehicle drivers. Predictive-forward-collision-warning (PFCW) is an emerging technology that enhances driving safety by providing traffic information beyond the driver’s line of sight. But the influence of PFCW on their decision behavior remains unclear. Therefore, it is necessary to investigate the leading driving competence of heterogeneous driver groups under the influence of PFCW. This study established a test platform for the connected mixed platoon using driving simulation technology. Subsequently, it developed a connected human–machine interface incorporating PFCW functionality and recruited 36 participants to experiment. The experiment aimed to analyze decision behaviors during the emergency braking event involving preceding vehicles. The latent profile analysis model was employed to identify heterogeneous driver groups. Moreover, the cumulative prospect theory was used to characterize drivers’ decision behaviors. The results indicated that PFCW encouraged drivers to maintain a larger distance from the preceding vehicle and to exhibit smoother braking behavior. However, the group of older, skilled drivers who were subjectively concerned about the driving status of following vehicles exhibited higher driving risk as the leading vehicle driver when PFCW was absent. Overall, this study enhances the understanding of longitudinal decision behavioral performance among heterogeneous driver groups in the presence of PFCW. It offers insights for subsequent customization of the selection criteria for leading vehicle drivers, and the tailored design of PFCW based on the characteristics and behavioral preferences of these heterogeneous driver groups.

Virtual simulation testing is crucial for ensuring automated vehicles safety, which offers low cost and good repeatability. The key is to test in various virtual driving scenarios, but often fails to strike a balance between scenario coverage and test efficiency. To address this issue, we propose a scenario generation method based on a Genetic Algorithm optimized Hamiltonian Monte Carlo sampling approach. Specifically, a Markov chain is constructed converging to the joint probability density distribution function of scenario parameters. By defining a Hamiltonian function with a potential energy term related to the posterior distribution and a kinetic energy term, the sampling process moves efficiently towards high probability regions and achieves faster convergence to the target distribution. Moreover, Jensen-Shannon divergence between generated samples and raw data is proposed to evaluate the scenario coverage, and used as the objective function in Genetic Algorithm to optimize the algorithm parameters. The proposed method is validated by lead vehicle deceleration scenarios generation, where an S-shaped deceleration model is proposed to parameterize the scenario and 22,343 segments extracted from a naturalistic driving dataset are used to calibrate the scenario parameters. Subsequently, the joint probability distribution density function of scenario parameters fitted by a gaussian mixture model is imported into our proposed method. As the results, 1,649 concrete lead vehicle deceleration scenarios are generated with a coverage of 99.14% for the dataset. Compared with the previous Markov Chain Monte Carlo sampling method, our method achieved 9 times higher coverage with 28 times fewer scenarios.

Motion control of autonomous vehicles (AVs) that considers nonlinear dynamics and multidimensional motion coupling characteristics represents a critical research direction, particularly for vehicles operating under extreme conditions. However, the nonlinearity of model and the time-varying characteristics of reference states make control law design challenging, often resorting to computationally expensive approaches such as model predictive control (MPC). This paper proposes an explicit nonlinear control design framework for optimal trajectory tracking of AVs. Specifically, taking the three-degree-of-freedom vehicle dynamics model as an example, we first augment the system state to yield an affine representation, followed by input-output feedback linearization. The discrete-time linearized system is then augmented with reference states from the preview horizon, and a linear quadratic regulator is designed to provide an analytical solution for the virtual control inputs. Finally, the original control inputs are computed using the feedback linearization control law. We performed simulations to evaluate the effectiveness of our proposed approach and compared it against MPC. Results indicate that our proposed approach achieves tracking accuracy, smoothness, and robustness comparable to MPC, while significantly reducing computational requirements, with a computing time of nearly 0 ms.

Achieving high-level autonomous driving poses significant challenges, primarily due to the extensive and time-consuming testing required for low-probability abnormal behavior scenarios. Existing simulation platforms are limited by microscopic traffic flow models based on car-following and lane-changing theories (focused on the behavior of individual vehi-cles), which limits the implementation of complex and diverse abnormal behaviors such as road deviation and cutting in. This paper proposes an Abnormal behavior Generation model based on Event Probability triggers and Static paths (AGEPS model), which is capable of continuously and automatically generating abnormal behaviors without disrupting normal traffic flow. The AGEPS model is computationally efficient and scalable, as it decouples path optimization and trajectory tracking from obstacle avoidance, using explicit control law for both tracking and avoidance. The paper demonstrates three typical abnormal behaviors: Overtaking on the Right (OOR), Driving on the Lane Line (DOL), and Sudden Braking (SUB), indicating that the AGEPS model effectively generates these behaviors by selecting and tracking target static paths while adjusting lateral offsets and desired speeds. Experimental results validate the AGEPS model's effectiveness and its ability to generate abnormal behaviors continuously. Simulation results indicate that the AGEPS model reduces single-step computation time (including decision-making and control) by over 92.8 % compared to the MPC controller, with a single-step execution time of just 4 ms.

Sudden traffic hazards trigger collision-avoidance behaviors in drivers that can significantly impact vehicle dynamics, potentially conflicting with the existing advanced driver assistance systems (ADAS), such as autonomous emergency braking and steering. This behavior can lead to unexpected vehicle movements, further complicating the situation and elevating the risk of accidents. Understanding and tailoring the inference of drivers’ perception-response time (PRT) is essential for optimizing ADAS activation in intelligent vehicles. This approach allows customization for individual drivers, improving safety and ensuring that interventions are personalized and minimally disruptive to normal driving patterns. To achieve this objective, this study performs high-fidelity simulation experiments to gather a comprehensive multidimensional dataset on drivers’ responses in safety-critical scenarios, primarily focusing on PRT and its influencing factors. Using the collision-avoidance behavior data, a driver evidence accumulation model is created to explain PRT distribution and facilitate real-time personalized inferences. We also analyze the relationship between model parameters and real-world physical significance, demonstrating that driver decisions rely on visual evidence accumulation influenced by dynamic interactions in different scenarios. Our proposed model, by offering a detailed understanding of drivers’ perceptual and decision-making processes, aids in developing personalized driver assistance system activation recommendations. This approach seeks to create personalized and adaptive systems within intelligent vehicles, thereby reducing human-machine conflicts and improving the overall safety of intelligent transportation systems.

Motion prediction is crucial for autonomous vehicles to operate safely in complex traffic environments. Extracting effective spatiotemporal relationships among traffic elements is key to accurate forecasting. Inspired by the successful practice of pretrained large language models, this paper presents SEPT, a modeling framework that leverages self-supervised learning to develop powerful spatiotemporal understanding for complex traffic scenes. Specifically, our approach involves three masking-reconstruction modeling tasks on scene inputs including agents' trajectories and road network, pretraining the scene encoder to capture kinematics within trajectory, spatial structure of road network, and interactions among roads and agents. The pretrained encoder is then finetuned on the downstream forecasting task. Extensive experiments demonstrate that SEPT, without elaborate architectural design or manual feature engineering, achieves state-of-the-art performance on the Argoverse 1 and Argoverse 2 motion forecasting benchmarks, outperforming previous methods on all main metrics by a large margin.

There will be a time when automated vehicles coexist with human-driven ones. Understanding how drivers assess driving risks and modeling their differences is crucial for developing human-like and personalized behaviors in automated vehicles, gaining people’s trust and acceptance. However, existing driving risk models are usually developed at a statistical level, and no single model can accurately describe and explain the variations in risk perception among drivers. We propose a concise yet effective model known as the Potential Damage Risk (PODAR) model, which provides a universal and physically meaningful structure for estimating driving risk and explaining the reasons for differences in risk perception. Leveraging an open-access dataset collected from an obstacle avoidance experiment, this paper establishes individual risk perception models for drivers with high fitness performances. We conclude that the variations in risk perception among drivers stem from their assessments of potential damage, accounting for the uncertainty in both temporal and spatial dimensions. Our findings offer an explanation for human risk perceptions and present a promising risk model for autonomous vehicles to develop human-like behaviors and personalized services.

End-to-end autonomous driving provides a feasible way to automatically maximize overall driving system performance by directly mapping the raw pixels from a front-facing camera to control signals. Recent advanced methods construct a latent world model to map the high dimensional observations into compact latent space. However, the latent states embedded by the world model proposed in previous works may contain a large amount of task-irrelevant information, resulting in low sampling efficiency and poor robustness to input perturbations. Meanwhile, the training data distribution is usually unbalanced, and the learned policy is challenging to cope with the corner cases during the driving process. To solve the above challenges, we present aSEManticMasked recurrent world model (SEM2), which introduces a semantic filter to extract key driving-relevant features and make decisions via the filtered features, and is trained with a multi-source data sampler, which aggregates common data and multiple corner case data in a single batch, to balance the data distribution. Extensive experiments on CARLA show our method outperforms the state-of-the-art approaches in terms of sample efficiency and robustness to input permutations.

Deep reinforcement learning (RL) is a powerful approach for solving optimal control problems. However, RL-trained policies often suffer from the action fluctuation problem, where the consecutive actions significantly differ despite only slight state variations. This problem results in mechanical components' wear and tear and poses safety hazards. The action fluctuation is caused by the high Lipschitz constant of actor networks. To address this problem, we propose a neural network named LipsNet. We propose the Multi-dimensional Gradient Normalization (MGN) method, to constrain the Lipschitz constant of networks with multi-dimensional input and output. Benefiting from MGN, LipsNet achieves Lipschitz continuity, allowing smooth actions while preserving control performance by adjusting Lipschitz constant. LipsNet addresses the action fluctuation problem at network level rather than algorithm level, which can serve as actor networks in most RL algorithms, making it more flexible and user-friendly than previous works. Experiments demonstrate that LipsNet has good landscape smoothness and noise robustness, resulting in significantly smoother action compared to the Multilayer Perceptron.

Intersection is one of the most accident-prone urban scenarios for autonomous driving wherein making safe and computationally efficient decisions is non-trivial. Current research mainly focuses on the simplified traffic conditions while ignoring the existence of mixed traffic flows, i.e., vehicles, cyclists and pedestrians. For urban roads, different participants lead to a quite dynamic and complex interaction, posing great difficulty to learn an intelligent policy. This paper develops the dynamic permutation state representation in the framework of integrated decision and control (IDC) to handle signalized intersections with mixed traffic flows. Specially, this representation introduces an encoding function and summation operator to construct driving states from environmental observation, capable of dealing with different types and variant number of traffic participants. A constrained optimal control problem is built wherein the objective involves tracking performance and the constraints for different participants, roads and signal lights are designed respectively to assure safety. We solve this problem by gradient-based optimization, wherein the reasonable state will be given by the encoding function and then served as the input of policy and value function. An off-policy training is designed to reuse observations from driving environment and backpropagation through time is utilized to update the policy function and encoding function jointly. Verification result shows that the dynamic permutation state representation can enhance the driving performance of IDC, including comfort, decision compliance and safety with a large margin. The trained driving policy can realize efficient and smooth passing in the complex intersection, guaranteeing driving intelligence and safety simultaneously.