Scenario-based testing is an important part in intelligent vehicle (IV) development. The data volume of collected test scenarios is extremely large, and directly using all collected scenarios to test IVs will lead to extremely low testing efficiency. To solve this problem, a criticality assessment model (CAM) for IV test scenario based on interactive field feature (IFF) and hypergraph learning is proposed to quantify the test scenario criticality to improve the test efficiency. The IFF is constructed based on the potential field-based method to integrally consider the multidimensional coupling of scenario elements. In addition, the interaction between the fields generated by the ego vehicle and the driving environment is modeled based on Delaunay triangulation discretization method to accurately quantify the driving environment risk to the ego vehicle. The node and hyperedge of the hypergraph are used to model the individual dynamic evolution and group interaction characteristics of vehicles, respectively. Subsequently, a hypergraph learning network is constructed to extract features from the built hypergraph, IFF and traffic elements. Finally, the effectiveness, reasonableness and accuracy validation experiments are designed to validate the proposed CAM. Ablation experiment results show that the proposed IFF and hypergraph learning network enhance the CAM accuracy. The reasonableness validation results show that the proposed CAM can better find critical test scenarios than time-to-collision and time-head-way methods. The accuracy of the proposed CAM is compared through four real validation scenarios in the proving ground. The comparison results show that the proposed CAM can accurately output the quantified test scenario criticality.

Trajectory planning is crucial for ensuring the safety of intelligent vehicles (IVs) in autonomous driving, especially in complex traffic environments where perception and control uncertainties increase collision risks. Existing risk-aware planning methods often fail to capture the full scope of uncertainties through risk assessment and struggle to consistently integrate risk information throughout the trajectory generation process. Risk factors are often overshadowed by smoothness and dynamic feasibility considerations, leading to excessively high-risk trajectories that fail to meet human driving expectations for caution. To address these challenges, we propose a cognitive risk-aware hierarchical trajectory planning method that adaptively regulates driving caution based on cognitive risk. We introduce cognitive risk assessment using two fields: the anisotropic objective risk field, which accounts for the size and comprehensive motion uncertainty of surrounding obstacles, and the driving cognition field, which considers the IV motion trends and reflects its proactive cognition of objective risks. By fusing these fields, we calculate cognitive risk and incorporate it into a planning framework that combines trajectory search and optimization. By explicitly considering cognitive risk constraints in both stages, the method achieves complete cognitive risk awareness, generating adaptive, safe, and dynamically feasible smooth trajectories. Experimental results in various scenarios demonstrate the effectiveness and superiority of the proposed method. By incorporating cognitive risk assessment, the IV exhibits more cautious driving behavior, aligning with human expectations. Compared to three state-of-the-art methods, our method improves the minimum time-to-collision by over 20%, reduces lane-crossing time by more than 10%, and decreases the average yaw rate by over 12%. In summary, the proposed method ensures higher safety, improves lane-changing efficiency and smoothness.

Longitudinal control systems in intelligent connected vehicle (ICV) significantly reduce driving workload; however, most existing designs overlook the driver’s psychological perception of car-following safety. This neglect can elevate physiological stress and erode trust in the system. To address this, this paper proposes a human-centered Connected Adaptive Cruise Control (C-ACC) strategy that integrates the driver’s individualized cognitive safety analysis into the control loop within a connected environment. Using naturalistic driving data that jointly capture traffic context, vehicle states, and driver physiology, we construct an individualized safety boundary in the speed–distance domain. The boundary is parameterized by a Sigmoid function fitted to physiological activation features and is incorporated as a soft constraint within a Model Predictive Control (MPC) framework. Crucially, the controller leverages Vehicle-to-Vehicle (V2V) communication to optimize tracking performance while proactively limiting states associated with elevated activation. The strategy was validated through driver-in-the-loop experiments using two real vehicles enabled with V2V communication. Experimental results demonstrate that, compared with baseline strategies, the proposed approach effectively reduces physiological activation and psychological tension while ensuring ride comfort and control stability, thereby enhancing the driver’s perceived safety and trust. This framework provides a principled path for personalized C-ACC design based on cognitive state estimation and is scalable to broader connected automated driving applications.

Long-tail scenarios are of great significance in improving the testing efficacy of autonomous vehicles, but they are difficult to collect due to their inherent randomness and high risk. The low-cost, high-value videos of vehicle-perspective accidents stored in tachographs provide a potentially important source for long-tail data collection. However, the lack of multisource synchronization information and the unknown, diverse camera parameters in tachograph videos make it difficult for the existing methods to accurately extract key information. In response to these issues, this work proposes a vehicle-perspective accident scenario restoration framework based on tachograph videos. First, using preprocessed accident videos as the input, a scenario semantic understanding module was constructed to help extract the pixel trajectories, conduct parameter calibration, and obtain pixel road boundaries. Then, robust detection and tracking algorithms were used to extract the pixel trajectories. Furthermore, a high-precision calibration algorithm was proposed based on the Geometrically Salient Feature Bundle Adjustment (GSFBA). Finally, the camera parameters were used to perform an inverse perspective transformation on the pixel coordinates, the actual road boundaries were extracted based on the mapping strength of the pixel road boundaries, and the trajectory information was optimized using a systematic trajectory processing module. The proposed method was verified at multiple levels based on both two simulation scenarios and two real-world scenarios. The results show that the proposed framework in this work can obtain the key information of the scenario with a high degree of restoration accuracy. It was effectively applied to the restoration process of accident scenarios.

The ultrasonic radar is a crucial part of the automatic parking system (APS) and requires thorough testing to ensure safety. The field operational test (FOT) is expensive and inefficient, making simulation testing the preferred method for evaluating autonomous driving systems. However, a gap remains between the performance of current simulation ultrasonic radar models and the real-world ultrasonic radars in real driving environments. To address these challenges, an ultrasonic radar in-the-loop accelerated test method for APS is proposed. First, a high-fidelity ultrasonic radar in-the-loop test bench is established and validated. To reduce the high cost and time duration of hardware-in-the-loop (HIL) testing, a Bayesian optimization (BO)-based accelerated testing framework is designed, minimizing test numbers by using the prior information to guide the search process. Finally, experiments are conducted using the ultrasonic radar in-the-loop test bench to compare the Bayesian accelerated test with the combination test (CT) for the automatic parking algorithm. The test results indicate that the ultrasonic radar in-the-loop test bench demonstrates greater authenticity and accuracy than the simulation software, while the HIL testing offers improved efficiency and safety compared to the FOT. Moreover, the efficiency of the Bayesian accelerated test is 4.9 times greater than that of the CT while still ensuring adequate scenario coverage.

To ensure that the camera functions (CFs) of intelligent vehicles can operate normally in rain and fog, sufficient tests for CFs are indispensable. In this work, the synthetic image generation model (SIGM) is proposed to generate images to synthesize rain and fog effects for testing CFs. First, to synthesize the rain blur effects on raw images, the geometric-physical fusion model is proposed based on the properties of raindrop microparticles and vehicle features. In this model, the position distribution of raindrops is determined by the geometric model part, and the image pixel effects are realized by the physical model part. Second, the atmospheric scattering model is used to synthesize the fog effect and contains two parts: an airlight estimation model to estimate airlight values and a transmittance estimation model, where the corrected image depth and fog microphysical information based on the Mie theory are used to determine the fog transmittance. Finally, SIGM is validated through real rain and fog in image quality, testing CFs with synthetic images and videos. The result absolute error sum obtained with SIGM (0.45) is smaller than that of the simulation software (0.57) and another synthetic method (0.65), and all unrecognized targets identified by SIGM match the real test results, whereas both the simulation software and another synthetic method fail to accurately test CF. The experimental results indicate that the proposed SIGM has higher authenticity and reliability than testing using the simulation platform and has higher efficiency, controllability, repeatability, and safety than testing using real vehicle.

Accident scenarios with long-tail characteristics are essential for advancing autonomous vehicles (AVs) functionality. Integrating such scenarios into the training process enhances adaptability to complex situations. However, the scarcity of accident data, due to their randomness and collection challenges, limits this integration. To address this issue, a framework named CRADLE is proposed, leveraging causal reinforcement learning (RL) and deep learning for accident scenario generation. Under extremely limited data conditions, CRADLE enables the construction of a highly diverse accident scenario database while ensuring consistency in accident causation. First, a scenario knowledge graph is constructed, incorporating both a scenario graph and an accident causation graph. Then, a scenario graph temporal prediction model trained on real driving data, generates a sampling space for scenario graphs. Subsequently, a causal inference module is developed to establish a mapping and transformation mechanism between temporal scenario graphs and accident causation graphs. Finally, a graph similarity measurement method is introduced to guide RL model in orderly sampling within the scenario graph sampling space, ensuring causally controlled scenario generation. The proposed method is applied to generate accident scenarios for two lane-changing situations: simultaneous lane changes and multi-lane changes. These scenarios are incorporated into the closed-loop self-evolution process of the autonomous driving algorithms. Experimental results demonstrate that the constructed accident scenario databases significantly improve the algorithm adaptability, reducing AV collision rates by approximately 76%.

This article focuses on the path tracking control problem for autonomous ground vehicles under switched triggered and sensor attacks. Firstly, an interval type-2 Takagi-Sugeno fuzzy model is established to effectively approximate the tire dynamic nonlinearities and varying velocity in the path tracking control system, in which the random deception attack encountered in the sensor is considered. Secondly, a novel switched triggered communication mechanism is presented to decrease the frequency of signal transmission and save the network resources. The switched triggered mechanism includes both the time-triggered mode and event-triggered mode, which obey a Bernoulli distribution. Then, based on a positive Lyapunov-Krasovskii functional and matrix inequalities, a set of conditions are developed for the path tracking controller design to achieve the asymptotic stability and performance requirements. Finally, experimental results are presented to evaluate and validate the performance of the proposed path tracking control method.

Driving risk prediction is important for the development of intelligent vehicles (IVs), and the rise of human-like driving requires a driving risk prediction system to match the subjective risk cognitive characteristics of human drivers. In this study, a subjective driving risk prediction model (SDRPM) for IVs is proposed and applied to lane-changing (LC) conditions. We regard the cognitive risk of the human driver as the coupling result of environmental objective risk and driver subjective cognition at the spatial and temporal scales. Then, an objective anisotropic risk field was built to describe risks in traffic environments, and a subjective spatiotemporal cognition field to describe the cognitive characteristics of human drivers. By combining the two fields, the spatiotemporal distribution features of the human driver’s cognitive risk were obtained and used as model inputs, and SDRPM output the predicted subjective driving risk level (SDRL) of the human driver. To quantify the SDRLs, six participants were recruited to watch 1,213 driving videos and report the SDRLs they cognized. Participants were provided with 360-degree driving video around the test vehicle and virtual reality glasses to ensure the reliability of the obtained SDRLs. Verification results showed that SDRPM has good predicted accuracy and long advance predicted time, with 97.53% predicted accuracy, which can reach 95.06% at 2 s before the LC point. Compared with six state-of-the-art models, SDRPM can improve the predicted accuracy while reducing the dimensions of input features. In summary, SDRPM can ensure the driving safety, and improve user acceptance and trust in IVs.

The intelligent connected vehicle (ICV) is an organic combination of connectivity and intellectuality, making it possible to exchange information and drive autonomously. How to assess and describe the traffic risk for the ICV is a fundamental and crucial problem. Unlike the existing studies that only obtain instantaneous and quasi-static risk fields due to the separation between space and time, this study proposes a novel Spatial-Temporal Risk Field (STRF) that represents the dynamic driving risk from the perspective of time-space coupling for ICVs in dynamic traffic is proposed. In order to generate the field for the different traffic elements, the influential traffic elements are divided into concrete elements and abstract elements according to their features and influencing mechanisms. And then, A biased sweeping method (BSM) is developed for concrete elements, and a modeling method based on Gaussian distribution is developed for abstract elements. The results of the STRF for a traffic scenario are visualized and analyzed in two forms: complete form and slice form. The results show that the STRF can precisely describe the risk distribution of each traffic element in a specific and characteristic shape according to the spatial-temporal situation. Additionally, this study also provides an application example of the STRF in trajectory planning to demonstrate the applicability and availability of the STRF. The STRF-based trajectory planning method greatly benefits from the STRF and shows the potential ability of flexible and personalized trajectory planning. The STRF proposed in this paper can express the dynamics and continuity of the spatial-temporal risk and describe the instantaneous risk by extracting the spatial risk distribution at a certain moment. The STRF can be used in risk assessment, decision making, trajectory planning, driving behavior modeling, and automatic testing.

With the development of intelligent vehicles, more research has focused on achieving human-like driving. As an important component of intelligent vehicle control, car-following control should ensure safety, tracking, comfort while considering the acceptance of human drivers. In this paper, we propose a car-following control strategy$\boldsymbol {\pi }_{ \boldsymbol {Hybrid}}$based on a hybrid of reinforcement learning (RL) and supervised learning (SL). RL is used to achieve multi-objective collaborative optimization in car-following control, and SL is used to achieve human like car-following. Through the complementary advantages of the two learning methods,$\boldsymbol {\pi }_{Hybrid}$can achieve high performance car-following while matching the personalized car-following characteristics of human drivers. RL is used as the main framework of$\boldsymbol {\pi }_{Hybrid}$. In addition, the personalized car-following reference model (PCRM) of human drivers based on Gaussian mixture regression, and the motion uncertainty model of preceding vehicle (MUMPV) based on the sequence-to-sequence network are established and incorporated into the RL framework. PCRM can lead$\boldsymbol {\pi }_{Hybrid}$to learn the different characteristics of human drivers, and improve the anthropomorphism of$\boldsymbol {\pi }_{Hybrid}$; MUMPV enables$\boldsymbol {\pi }_{Hybrid}$to consider the dynamic changes of the traffic environment and to become more robust.$\boldsymbol {\pi }_{Hybrid}$is trained and tested on High D dataset, and the generalizability verification is based on the self-built real vehicle data collection platform. The results show that$\boldsymbol {\pi }_{Hybrid}$can match human drivers’ personalized car-following characteristics and can outperform human drivers in safety, comfort, and tracking of the preceding vehicle.

With the development of intelligent vehicle technology, many studies have been focused on developing human-like trajectory planning methods for automated driving systems. Although data-driven methods are widely used for human driver behavior learning, there have been fewer studies on realizing human-like trajectory planning by using the generation mechanism of driving behavior, especially under curve conditions, where the lane centerline has been denoted as a reference trajectory. In this paper, thirty-two skilled drivers were recruited to collect data under different curve conditions on a self-designed driver-in-the-loop system. The collected data are processed by dynamic time warping, trajectories with different lengths are warped and the abnormal data are removed. Based on the warped data, common characteristics and differences between left and right turning trajectories are compared and explored from the perspectives of drivers’ demand for turning performance and their visual attention mechanism. Then, by introducing the driver preview mechanism, two features with a strong ability to represent the generation mechanism of the driver’s curve driving behavior are introduced. Finally, the preview-based human-like trajectory planning model (PHTPM) is proposed, and it is verified and analyzed by comparative tests and generalizability tests. The results show that the introduction of the driver preview mechanism enables PHTPM to match the characteristics of skilled drivers accurately on left turnings and outperform them on right turnings.

Intelligent vehicle testing has been a hotspot in the field of intelligent vehicles. Due to the multi-dimensional parameters and the continuity of testing scenarios, a complete test of all scenarios requires a large amount of manpower and several material resources. In critical scenarios, deficiencies in an intelligent vehicle’s performance and defects of an algorithm can be exposed. Therefore, increasing the search efficiency and coverage of critical scenarios is key in improving scenario-based intelligent vehicle testing technology. In this study, a critical scenario search method for intelligent vehicle testing based on the social cognitive optimization (SCO) algorithm is proposed. This method has two main parts: global search and local search. The global search, based on the modified SCO algorithm, integrates the density peak clustering (DPC) algorithm and the cooling scheduling function, and aims to find all local aggregation areas of critical scenarios in a logical scenario space. The local search applies a multi-dimensional convolution algorithm to the global search results to find critical scenarios near the local aggregation areas. Finally, a longitudinal automatic driving algorithm is tested using the proposed method under a specified logical scenario in a simulation environment. The test results show that the proposed method can improve both the search efficiency and coverage of critical scenarios.

Vehicle trajectory prediction is a crucial but intricate problem for lateral driving assistance systems because of driver uncertainty. This article presents a probabilistic vehicle-trajectory prediction method based on a dynamic Bayesian network (DBN) model integrating the driver’s intention, maneuvering behavior, and vehicle dynamics. By selecting a most-relevant-feature vector using joint mutual information, we design a Gaussian mixture model- hidden Markov model and employ the model as a node in the DBN to identify the driver’s intention. Then, a reference path is generated using the road information. The uncertainties of drivers are captured in steering- and longitudinal-control using a stochastic driver model and a Markov chain, respectively. A vehicle dynamic model ensures that the predicted vehicle trajectory adheres to the vehicle dynamics, which improves the prediction accuracy. A particle filter is used to recursively estimate the vehicle trajectory, including position coordinates and the lateral distance from the vehicle center of gravity to the road edge. We evaluate the proposed DBN trajectory prediction method in both lane-keeping and lane-changing scenarios based on a dataset collected from a real-time dynamic driving simulator. Results show that the proposed method can achieve accurate long-term trajectory prediction.

The scenario-based test method is the research hotspot of automated vehicle (AV) validation and verification (V&V), and testing with hazardous scenarios is of important means. An Optimization Searching (OS) method for enhanced generation in hazardous scenarios is proposed in this paper to efficiently explore functional boundary scenarios in a huge logical state space. The method is computationally tractable, and its generated experimental parameters are optimized using past test results. The method includes five essential modules. The Exploration and Exploitation module uses theMulti-arm banditmethod to obtain the greatest sum of the$TTC^{\mathbf {-1}}$(Time To Collision). The Parameter Moving Probability Determination module uses an analytic hierarchy process to ensure that influential parameters are more likely to move. The Step Size Determination module is built withLevy-stepto find a greater number of hazardous scenarios. The Memory Function module is used to avoid repeat experiments that can reduce computing efficiency. The Result Analysis module creates a hazard parameter space for subsequent tests. We tested an ACC (Adaptive Cruise Control) algorithm with a specified logical scenario in the virtual environment built by PreScan. The results showed that the OS method can effectively discover the dangerous range with the tested ACC algorithm, and its test speed can reach more than five times that of an exhaustive algorithm without prior knowledge.

Human-machine shared control has become an effective cutting-edge approach to enhance driving safety and assist in the transition from manual to autonomous driving. However, driving authority allocation when conflict occurs between a human driver and a machine still represents an intractable problem. In order to solve this problem, this paper proposes an adaptive steering torque coupling framework to achieve the human-machine shared control with conflict resolution. Additionally, a driver trajectory prediction method considering human-machine interaction is established based on the extended state observer and long short-term memory network. In addition, the human-machine shared steering system is modeled based on a non-cooperative dynamic game by the prediction model method for path-tracking, and the obtained solution is given according to the Nash equilibrium. Particularly, a dynamic load allocation approach is designed to resolve human-machine conflict and relax the driver. In order to verify the strategy formed based on the proposed framework, a driver-in-the-loop experiment is conducted. The experimental results reveal that the proposed strategy can effectively reduce the human-machine conflict torque and ensure a driver has absolute control authority. Furthermore, the proposed strategy can also transfer the driving workload between the driver and machine, provide driving experience, or improve driving comfort when there is no conflict.

Intelligent vehicle testing and validation technology is a hotspot in the field of intelligent vehicles. Millimeter-wave radar has been widely used in intelligent vehicles and advanced driving assistant systems (ADASs). Owing to low efficiency, high maintenance cost and limited scenario types, the traditional real-vehicle-testing can make it difficult to meet the requirements of intelligent vehicle testing. Also, the degree of reality of millimeter-wave radar sensor models in the virtual test platform is not convincing enough. To solve the mentioned problems, the millimeter-wave radar in-the-loop (mmRil) testing for intelligent vehicles is proposed in this study. First, the radar echo simulation method is developed. Next, based on the millimeter-wave flight time, Doppler frequency shift and the radar power attenuation equation, the mmRil test system is built. A geometric model of a radar target detection and a power attenuation model under bad weather conditions are established. Then, the accuracy of the testbench is verified. Finally, a typical intelligent vehicle control strategy is used for testing under various working conditions and bad weather using the mmRil testbench. The test results show that the mmRil testing has higher authenticity and accuracy than the simulation testing and can be used to test the intelligent vehicle algorithms under severe weather conditions. Compared with the real vehicle testing, the mmRil testing has higher efficiency, repeatability and safety. Therefore, the mmRil testing represents an indispensable testing method in the field of intelligent vehicle testing.

With the common use of lidar sensors on intelligent vehicles, the simulation of lidar in autonomous driving is necessary. This study proposes an innovative lidar modeling method, and introduces solutions for the simulation of the lidar detection function and its physical mechanism. The model consists of geometric and physical models, and can simulate both point clouds and targets. The geometric model addresses the spatial relationship between lidar and the environment. The physical model describes how the lidar detection mechanism may influence detection results. Signal attenuation and unwanted raw data caused by raindrops are the main consideration in the physical model. Characteristics of lidar signal attenuation in different weather conditions are modeled, and a simplified lidar equation is derived for use by lidar users, as opposed to designers. Unwanted raw data are simulated in a stochastic model employing the Monte Carlo method, where raindrop size and distance are sampled. The model is calibrated and validated with real lidar data. The application of the proposed lidar model for an autonomous emergency braking system is introduced.

There have been explosive developments in automatic driving in recent years. Several kinds of self-driving vehicles have been introduced, but drivers are generally required to stay alert and put their hands on the steering wheel during automatic driving, which means the vehicle is in a state of human-machine cooperative control. Hence personalization is needed to reduce conflicts between driver and machine. To achieve personalized automatic driving, a combined hierarchy learning framework (CHLF) based on gated recurrent units (GRU) and safety field is developed in this paper. Learning-based methods are gaining attention in the development of automatic vehicles. However, automatic driving based on neural networks is risky due to poor interpretability. To overcome this limitation, we divided the network into three layers according to function to achieve a hierarchy. We combine data- and mechanism-oriented methods to make the CHLF reliable and stable. Lane-changing (LC) driving data collected from real-vehicle field experiments are used to train the CHLF, which learns from the data to capture human driver behavior in LC. To verify the performance of the CHLF, a group of driver-in-the-loop experiments is conducted based on a driving simulation test bench. The results show that the CHLF can reduce driver steering output torque compared to the conventional method, which means there is less conflict between driver and machine during automatic LC, and the driver is more relaxed.

The exact and reliable understanding of driver behavior characteristics has a large potential contribution to the active safety control system, advanced driver assistance system, and intelligent traffic system. This study presents a driver behavior characteristics identification strategy based on bionic intelligent algorithms. First, a driver behavior data acquisition system is established to be used with test subjects of different driving skill levels for driving data acquisition. The preview optimal curvature model, which directly reflects the driver's behavior characteristics based on its parameters, is then chosen as the ideal driver behavior model and is identified through the genetic algorithm, particle swarm optimization, and back propagation neural network. Moreover, this paper discusses the application of driver behavior characteristics identification in an integrated chassis control system (ICC), which integrates active front steering with electronic stability control. Finally, simulations are performed to verify the identification results and the application in the ICC by PanoSim and MATLAB/Simulink software. Based on the results, the proposed identification strategy precisely characterized the driver behavior. As a result, its application in the ICC improved the path-following ability and vehicle stability performance of the drivers.