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.

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.

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.

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.

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 $\bm{\pi}_{\bm{Hybrid}}$ 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, $\bm{\pi}_{{Hybrid}}$ can achieve high performance car-following while matching the personalized car-following characteristics of human drivers. RL is used as the main framework of $\bm{\pi}_{{Hybrid}}$ . 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 $\bm{\pi }_{{Hybrid}}$ to learn the different characteristics of human drivers, and improve the anthropomorphism of $\bm{\pi }_{{Hybrid}}$ ; MUMPV enables $\bm{\pi }_{{Hybrid}}$ to consider the dynamic changes of the traffic environment and to become more robust. $\bm{\pi }_{{Hybrid}}$ 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 $\bm{\pi }_{{Hybrid}}$ can match human drivers’ personalized car-following characteristics and can outperform human drivers in safety, comfort, and tracking of the preceding vehicle.