Driving simulators have been widely used in driving behavior analysis and intelligent driving algorithm development. However, the validity of driving behavior data derived from driving simulators remains unclear. In this study, 30 Chinese drivers were recruited to participate in two experiments: on-road and simulator experiments. An instrumented vehicle and a high-fidelity simulator were used in the on-road and simulator experiments, respectively, to investigate the effects of high speed (60, 80, and 100 km/h) and a visual distraction task on the lateral driving performance, including lane deviation (LD), standard deviation of the lane position (SDLP) rate, standard deviation of the steering wheel angle (SDSWA) rate, and steering wheel reversal rates (SRRs) (at levels of 1.3° and 2.5°). It was found that the visual distraction task impaired the drivers' lane-keeping ability. Furthermore, the driving task had similar effects on the LD, SDLP rate, and SRRs (2.5°) in the on-road and simulator experiments. The effects of the driving speed on the LD, SDLP rate, and SDSWA rate were comparable in both driving environments. However, the results confirmed that even a high-fidelity driving simulator could not achieve perfect absolute validity. The results provided preliminary evidence that the high-fidelity driving simulator used in this study might be an effective tool for investigating the effects of visual distractions task on lateral driving behavior.

The low user acceptability of advanced driver assistance systems (ADASs) is one of the fundamental problems limiting their wider adoption. One of the key factors affecting the frequency of warning signals is the standard warning thresholds set in ADASs, which may not be compatible with all drivers owing to various driver characteristics affecting their cognition of risk, such as comfort, skill, and experience. The present study focuses on the lane change warning (LCW) system to evaluate driving styles according to the perception of lane change risk, and determine appropriate warning thresholds corresponding to the different driving styles. A theoretical lane change warning model is established to calculate the deceleration required for the rear approaching vehicle in the target lane. The results of risk assessment experiments conducted on an actual expressway using 15 proficient drivers are employed to evaluate the risk cognitions of the participants, where the participants are divided into four different driving styles according to their adaptive warning thresholds, which are denoted as very low threshold, low threshold, medium threshold, and high threshold driving styles. Signal detection theory is then employed to determine lane change warning thresholds appropriate to the different driving styles. Our results clearly demonstrate the disparate perceptions of lane change risk for the different drivers. Therefore, the LCW system should adopt different warning thresholds for different drivers according to their driving style. The findings provide evidence supporting the exploitation of driving styles for adopting different warning thresholds in the LCW system to enhance its user acceptability.

The accurate and prompt recognition of a driver’s cognitive distraction state is of great significance to intelligent driving systems (IDSs) and human-autonomous collaboration systems (HACSs). Once the driver’s distraction status has been accurately identified, the IDS or HACS can actively intervene or take control of the vehicle, thereby avoiding the safety hazards caused by distracted driving. However, few studies have considered the time–frequency characteristics of the driving behavior and vehicle status during distracted driving for the establishment of a recognition model. This study seeks to exploit a recognition model of cognitive distraction driving according to the time–frequency analysis of the characteristic parameters. Therefore, an on-road experiment was implemented to measure the relative parameters under both normal and distracted driving via a test vehicle equipped with multiple sensors. Wavelet packet analysis was used to extract the time–frequency characteristics, and 21 pivotal features were determined as the input of the training model. Finally, a bidirectional long short-term memory network (Bi-LSTM) combined with an attention mechanism (Atten-BiLSTM) was proposed and trained. The results indicate that, compared with the support vector machine (SVM) model and the long short-term memory network (LSTM) model, the proposed model achieved the highest recognition accuracy (90.64%) for cognitive distraction under the time window setting of 5 s. The determination of time–frequency characteristic parameters and the more accurate recognition of cognitive distraction driving achieved in this work provide a foundation for human-centered intelligent vehicles.