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.

Determining an appropriate time to execute a lane change is a critical issue for the development of Autonomous Vehicles (AVs).However, few studies have considered the rear and the front vehicle-driver’s risk perception while developing a human-like lane-change decision model. This paper aims to develop a lane-change decision model for AVs and to identify a two level threshold that conforms to a driver’s perception of the ability to safely change lanes with a rear vehicle approaching fast. Based on the signal detection theory and extreme moment trials on a real highway, two thresholds of safe lane change were determined with consideration of risk perception of the rear and the subject vehicle drivers, respectively. The rear vehicle’s Minimum Safe Deceleration (MSD) during the lane change maneuver of the subject vehicle was selected as the lane change safety indicator, and was calculated using the proposed human-like lane-change decision model. The results showed that, compared with the driver in the front extreme moment trial, the driver in the rear extreme moment trial is more conservative during the lane change process. To meet the safety expectations of the subject and rear vehicle drivers, the primary and secondary safe thresholds were determined to be 0.85 m/s² and 1.76 m/s², respectively. The decision model can help make AVs safer and more polite during lane changes, as it not only improves acceptance of the intelligent driving system, but also further ensures the rear vehicle’s driver’s safety.

Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of natural crossing sequence data of pedestrians and vehicles are first collected by a laser scanner and HD camera, then 1980 effective crossing samples of pedestrians are selected. Influencing parameter sets of pedestrian crossing intention are then obtained through statistical analysis. Finally, long short-term memory network with attention mechanism (AT-LSTM) model is proposed. Compared with the support vector machine (SVM) model, results show that when the pedestrian crossing intention is recognized 0 s prior to crossing, the recognition accuracy of the AT-LSTM model for pedestrian crossing intention is 96.15%, which is 6.07% higher than that of SVM model; when the pedestrian crossing intention is recognized 0.6 s prior, the recognition accuracy of AT-LSTM model is 90.68%, which is 4.85% higher than that of the SVM model. The determination of pedestrian crossing intention parameter set and the more accurate recognition of pedestrian intention provided in this work provide a foundation for future fully automated driving vehicles.

Real-time driver behavior detection (DBD) is essential for developing driver-centered human-vehicle co-driving systems. This paper proposes an accurate and easily implementable DBD method based on driver behavior images and deep learning. Convolutional neural networks have difficulty dealing with changes in driver behavior images (rotation, scale, and translation). They suffer from a tradeoff between accuracy and number of trainable parameters, noise interference in images of driver behavior, and the black-box problem of neural networks, limiting the effectiveness of current DBD methods. Therefore, we design a novel deep learning-based DBD method consisting of a deep deformable inverted residual network with an attention mechanism. Deformable convolution is used to deal with image rotation and translation. An inverted residual block and linear bottleneck approach based on depthwise separable convolution is used to reduce the number of trainable parameters while maintaining high accuracy. An attention mechanism based on soft thresholding is incorporated into the nonlinear transformation layers to extract driver behavior-relevant features. We also innovatively propose a visualization method to improve the interpretability of the proposed method. The experimental results show that the proposed method can accurately detect driver behavior (95.17% mAP on the Kaggle driving test dataset), significantly outperforming state-of-the-art methods regarding accuracy, real-time performance, and reliability.