A human-vehicle-environment closed-loop system is the core of the advanced driver-assistance system (ADAS), and the driver is an important part of the closed-loop system. Therefore, an in-depth study on driving habits and personalized driving-assistance systems is conducive to the realization of vehicle safety and intelligent driving. In this paper, we present a personalized vehicle lane-change assistance system integrated with a driver-behavior identification strategy that can select proper occasions and trajectories of lane changes. First, the driver-behavior data-acquisition system is designed and established. Based on this, the input data of different kinds of drivers along with vehicle signals are collected under typical working conditions. The drivers are classified utilizing factor analysis and a fuzzy c-means (FCM) clustering algorithm, and the identification of driver behavior is realized using a back-propagation (BP) neural network optimized by a particle swarm optimization (PSO) algorithm. Then, a personalized lane-change warning method based on surrounding multi-vehicle information is proposed. Next, based on a sinusoidal lane-change model, personalized lane-change trajectory planning is proposed, and trajectory tracking is realized by a model predictive control (MPC) algorithm. Finally, a co-simulation is carried out using MATLAB/Simulink and CarSim, and the results show that the lane-change assistance system based on driver behavior can provide personalized occasions and trajectories of lane changes. The proposed trajectory-tracking control algorithm meets the requirements of intelligent vehicle trajectory-tracking control, and has good control effects.

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.

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.

Positioning technology is an important component of environmental perception and the basis of decision-making and motion control for intelligent vehicles. Current independent positioning methods inevitably have defects and poor environmental adaptability. To improve positioning accuracy and stability using low-cost sensors, a multi-sensor fusion positioning strategy for intelligent vehicles using global pose graph optimization is proposed in this paper. This strategy complements the advantages of multiple sensors to obtain global optimal positioning results, and it can cope with a variety of challenging working conditions. Considering the fast response of the inertial measurement unit (IMU) and the advantages of a camera without drift, this strategy realizes the tightly coupled fusion of vision and the IMU to obtain the optimal pose of visual-inertial odometry (VIO) by nonlinear optimization. Moreover, this strategy refines the modeling of the IMU, and proposes a keyframe selection strategy to deal with driving in a straight line without obvious feature changes, turning, and other driving conditions. Taking into account the versatility of the global positioning system (GPS) and the low cost of the network real-time kinematic (NRTK) positioning, the output of VIO and GPS/NRTK is adaptively fused through global pose graph optimization to maintain global consistency. Finally, the multi-sensor fusion positioning strategy is verified using the KITTI dataset on the ROS simulation platform. The results show that the multi-sensor fusion positioning strategy for intelligent vehicles has better accuracy and environmental adaptability at a lower cost than state-of-the-art strategies.