Lane changing is considered as one of the most dangerous driving behaviors because drivers have to deal with the traffic conflicts on both the current and target lanes. This study aimed to propose a method of predicting the driving risks during the lane-changing process using drivers’ physiology measurement data and vehicle dynamic data. All the data used in the proposed model were obtained by portable sensors with the capability of recording data in the actual driving process. A hidden Markov model (HMM) was proposed to link driving risk with drivers’ physiology information and vehicle dynamic data. The two-factor indicators were established to evaluate the performances of eye movement, heart rate variability, and vehicle dynamic parameters on driving risk. The standard deviation of normal to normal R–R intervals of the heart rate (SDNN), fixation duration, saccade range, and average speed were then selected as the input of the HMM. The HMM was trained and tested using field-observed data collected in Xi’an City. The proposed model using the data from the physiology measurement sensor can identify dangerous driving state from normal driving state and predict the transition probability between these two states. The results match the perceptions of the tested drivers with an accuracy rate of 90.67%. The proposed model can be used to develop proactive crash prevention strategies.

Due to the influence of altitude change on a driver’s heart rate, it is difficult to estimate driving fatigue using heart rate variability (HRV) at a road segment with frequent and rapid altitude change. Accordingly, a novel method of driving fatigue estimation for driving at plateau area with frequent altitude changes is proposed to provide active safety monitoring in real time. A naturalistic driving experiment at Qinghai-Tibet highway was conducted to collect drivers’ electrocardiogram data and eye movement data. The results of the eye movement-based method were selected to enhance the HRV-based driving fatigue degree estimation method. A correction factor was proposed to correct the HRV-based method at the plateau area so that the estimation can be made via common portable devices. The correction factors for both upslope and downslope segments were estimated using the field experiment data. The results on the estimation of revised driving fatigue degree can describe the driver’s fatigue status accurately for all the road segments at the plateau area with altitudes from 3540 to 4767 m. The results can provide theoretical references for the design of the devices of active safety prevention.

Cooperative lane-change decisions at freeway merging areas can resolve local vehicle conflicts. A locally acceptable interaction does not necessarily ensure the maintenance and recovery of mainline traffic performance after a lane-change disturbance. Individual utility-oriented cooperation may even induce excessive yielding and amplify disturbances to upstream traffic. To address this problem, a hierarchical cooperative lane-change decision model for connected and automated vehicles (CAVs) is developed to enhance mainline traffic resilience. A coupled resilience indicator system of safety and efficiency is first established to characterize mainline performance maintenance and recovery throughout the lane-change disturbance process. The hierarchical cooperative lane-change decision model consists of a behavior-decision layer and a system-optimization layer. The behavior-decision layer develops a Stackelberg game to determine the equilibrium strategy between lane change and yielding, with resilience incorporated into the vehicle payoff. The system-optimization layer optimizes the lane-change speed and acceleration according to mainline speed fluctuations and vehicle delay. The coupled resilience of the mainline system is further used to determine the final execution parameters from the Pareto solution set. The results show that the proposed hierarchical cooperative lane-change model increases the minimum mean safety performance of the mainline system from approximately 0.66 to 0.78. System resilience varies nonmonotonically with the CAV penetration rate: the coupled resilience decreases to approximately 0.872 at a 60% penetration rate and recovers to approximately 0.887 at an 80% penetration rate. In addition, the increase in cooperation intensity from 0.3 to 0.9 improves system safety resilience by up to 2.6%. These results demonstrate the effectiveness of hierarchical coordination and reveal the influence of heterogeneous mixed traffic on mainline traffic resilience.