The Emergence Characteristics of Driver’s Intentions Influenced by Different Emotions

Drivers’ behavioral intentions can affect traffic safety, vehicle energy use, and gas emission. Drivers’ emotions play an important role in intention generation and decision making. Determining the emergence characteristics of driver intentions influenced by different emotions is essential for driver intention recognition. This study focuses on developing a driver’s intention emergence model with the involvement of driving emotion on two-lane urban roads. Driver emotions were generated using various ways, including visual stimuli (video and picture), material incentives, and spiritual rewards. Real and virtual driving experiments were conducted to collect the multi-source dynamic data of human–vehicle–environment. The driver intention emergence model was constructed based on an artificial neural network, to identify the influences of drivers’ emotions on intention, as well as the evolution characteristics of drivers’ intentions in different emotions. The results show that the proposed model can make accurate predictions on driver intention emergence. The findings of this study can be used to improve drivers’ behavior, in order to create more efficient and safe driving. It can also provide a theoretical foundation for the development of an active safety system for vehicles and an intelligent driving command system.

Shared Steering Control for Lane Keeping and Obstacle Avoidance Based on Multi-Objective MPC

This paper presents a shared steering control framework for lane keeping and obstacle avoidance based on multi-objective model predictive control. One of the control objectives is to track the reference trajectory, which is updated continuously by the trajectory planning module; whereas the other is to track the driver’s current steering command, so as to consider the driver’s intention. By adding the two control objectives to the cost function of an MPC shared controller, a smooth combination of the commands of the driver and the automation can be achieved through the optimization. The authority of the driver and the automation is allocated by adjusting the weights of the objective terms in the cost function, which is determined by the proposed situation assessment method considering the longitudinal and lateral risks simultaneously. The results of the CarSim-Matlab/Simulink joint simulations show that the proposed shared controller can assist the driver to complete the tasks of lane keeping and obstacle avoidance smoothly while maintaining a good level of vehicle stability.

Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution

Pressure sensors are good candidates for measuring driver postural information, which is indicative for identifying driver’s intention and seating posture. However, monitoring systems based on pressure sensors must overcome the price barriers in order to be practically feasible. This study, therefore, was dedicated to explore the possibility of using pressure sensors with lower resolution for driver posture monitoring. We proposed pressure features including center of pressure, contact area proportion, and pressure ratios to recognize five typical trunk postures, two typical left foot postures, and three typical right foot postures. The features from lower-resolution mapping were compared with those from high-resolution Xsensor pressure mats on the backrest and seat pan. We applied five different supervised machine-learning techniques to recognize the postures of each body part and used leave-one-out cross-validation to evaluate their performance. A uniform sampling method was used to reduce number of pressure sensors, and five new layouts were tested by using the best classifier. Results showed that the random forest classifier outperformed the other classifiers with an average classification accuracy of 86% using the original pressure mats and 85% when only 8% of the pressure sensors were available. This study demonstrates the feasibility of using fewer pressure sensors for driver posture monitoring and suggests research directions for better sensor designs.

The Role of Water Depth Perception in Shaping Car Drivers’ Intention to Enter Floodwaters: Experimental Evidence

Floods are one of the most lethal natural hazards. Recent studies show that in a large percentage of flood-related fatalities, victims engage in risk-taking behavior by getting deliberately in contact with floodwaters. This study integrates behavioral psychology and situational environmental factors with the aim to examine why individuals undertake such risky behavior. In particular, we draw on the theory of planned behavior (TPB) to link water depth perception with the intention of car drivers to enter floodwaters. The hypotheses on which the study was based were that the depth of the water adversely affects the driver’s intention to enter floodwaters, and that this effect is mediated by a behavior-favorable attitude, a behavior-favorable subjective norm, and perceived behavioral control. Further, to understand the conditions under which this process works, the moderating role of past behavior in the above relationships is also examined. Results from an experimental study (n = 1940) show that water depth perception affects intention. Attitude, perceived behavioral control, and normative beliefs operate as the underlying psychological mechanism that leads to the mitigation of intention in higher water depth situations. Interestingly, past risk-taking behavior is found to be a significant condition under which this process works, by mostly affecting individuals’ attitudes. Mediation and moderated mediation analyses were conducted to estimate causal relationships. The findings provide evidence of the significant interaction that environmental, psychological, and precedent behavioral factors have on behavioral intentions.

An Evaluation Method of Mode Switching Quality for Double-Belt Continuously Variable Transmission

In order to improve the transmission efficiency and carrying capacity of conventional single-belt continuously variable transmission (CVT), one new type of dual-belt CVT is proposed in this paper. Under the situation that this new dual-belt CVT should be switched between single- and dual-belt modes frequently according to driver’s intention and road conditions, so five objective evaluation indexes of mode switching quality for the dual-belt CVT are proposed, considering the aspects of vehicle power, comfort, and transmission durability comprehensively. Then, the objective evaluation model of mode switching quality is established by the BP neural network optimized by the genetic algorithm. It is found that the prediction results are consistent with the subjective evaluation. After analyzing the influence of the selected five evaluation indexes on the prediction results, it is obvious that these five evaluation indexes of mode switching quality for dual-belt CVT are reasonable.

Shared control strategy based on trajectory following and driver intention optimization

During the emergency braking process on the split-[Formula: see text] road, the lateral stability of the vehicle is poor, and the intervention of ABS will cause corresponding lateral disturbance. It is difficult for the driver to control the vehicle accurately. Especially at the end of the braking process, due to the withdrawal of ABS, the increase in braking pressure causes the longitudinal force of the tires on both sides to be inconsistent, which reduces the stability of the vehicle at this time. This paper proposed a shared control strategy to solve the related problems. First, a segmented active steering strategy is used in the driver’s intention optimization algorithm to optimize the driver’s actions in time at the initial stage of the braking process and to optimize the lateral stability of the vehicle by tracking the estimated tire slip angle at the end of the braking process. Then, according to the path envelope based on the driver’s path error neglecting feature and the dynamic state of the vehicle, a flexible control transfer mechanism is established. The trajectory following algorithm based on linear quadratic regulator is used to correct the driver’s intention optimization algorithm according to the flexible control transfer mechanism.

An Automatic Emergency Braking Model considering Driver’s Intention Recognition of the Front Vehicle

Driver’s intention of the front vehicle plays an important role in the automatic emergency braking (AEB) system. If the front vehicle brakes suddenly, there is potential collision risk for following vehicle. Therefore, we propose a driver’s intention recognition model for the front vehicle, which is based on the backpropagation (BP) neural network and hidden Markov model (HMM). The brake pedal, accelerator pedal, and vehicle speed data are used as the input of the proposed BP-HMM model to recognize the driver’s intention, which includes uniform driving, normal braking, and emergency braking. According to the recognized driver’s intention transmitted by Internet of vehicles, an AEB model for the following vehicle is proposed, which can dynamically change the critical braking distance under different driving conditions to avoid rear-end collision. In order to verify the performance of the proposed models, we conducted driver’s intention recognition and AEB simulation tests in the cosimulation environment of Simulink and PreScan. The simulation test results show that the average recognition accuracy of the proposed BP-HMM model was 98%, which was better than that of the BP and HMM models. In the Car to Car Rear moving (CCRm) and Car to Car Rear braking (CCRb) tests, the minimum relative distance between the following vehicle and the front vehicle was within the range of 1.5 m–2.7 m and 2.63 m–5.28 m, respectively. The proposed AEB model has better collision avoidance performance than the traditional AEB model and can adapt to individual drivers.

Research on Starting Control Method of New-Energy Vehicle Based on State Machine

In order to improve the starting smoothness of new-energy vehicles under multiple working conditions and meet the driving intention better, and to make the control strategy have high portability and integration, a starting control method for vehicle based on state machine is designed. Based on inclination, starting of vehicle is divided into three working conditions: flat road, slight slope and steep slope. The method of vehicle starting control is designed, which includes five control states: default state control, torque pre-loading control, anti-rollback control, pedal control and PI (Proportion-Intergral) creep control. The simulation is carried out under the conditions of flat road, slight slope and steep slope. In terms of flat road and light slope, the vehicle travels below 3 km/h according to the driver’s intention, the speed is stable at 8 km/h during the creeping control phase and the jerk is lower than 5 m/s3. In terms of steep slope, the speed is controlled at 0 km/h basically and the 10 s-rollback distance is less than 0.04 m. The results show that the strategy can fully meet the driver’s intention with lower jerk, better dynamic and stability, and the method can achieve the demand of new-energy vehicle starting control.