Comfort is a significant factor that affects passengers’ choice of autonomous vehicles. The comfort of an autonomous vehicle is largely determined by its control algorithm. Therefore, if the comfort of passengers can be predicted based on factors that affect comfort and the control algorithm can be adjusted, it can be beneficial to improve the comfort of autonomous vehicles. In view of this, in the present study, a human-driven experiment was carried out to simulate the typical driving state of a future autonomous vehicle. In the experiment, vehicle motion parameters and the comfort evaluation results of passengers with different physiological characteristics were collected. A single-factor analysis method and binary logistic regression analysis model were used to determine the factors that affect the evaluation results of passenger comfort. A passenger comfort prediction model was established based on the bidirectional long short-term memory network model. The results demonstrate that the accuracy of the passenger comfort prediction model reached 84%, which can provide a theoretical basis for the adjustment of the control algorithm and path trajectory of autonomous vehicles.

Identifying distracted drivers is crucial for enhancing driving safety and advancing intelligent driver assistance systems. Recently, researchers have applied Convolutional Neural Network (CNN) and Vision Transformer (ViT) models for driver state decision. However, both models often suffer from several issues such as numerous parameters and low detection efficiency. To address these challenges, this study proposes the Convolution Vision Transformer (CoViT) model for distracted driver identification, leveraging techniques such as Low Complexity Attention Mechanism (LCAM), Multi-scale Dilation Convolution (MSDC), and Depth Separable Convolution (DSC). Moreover, the CoViT model features a typical “pyramid” structure, enabling effective feature extraction across different scales. Subsequently, the proposed system is trained and evaluated using the publicly available driving behavior datasets SFD2 and 100-Driver, as well as real-world road experiments. Experimental results show that the CoViT model yields high recognition performance, with mean Accuracy (mAcc) scores of 95.17%, 97.89%, and 93.54% on the recorded dataset, SFD2 dataset, and 100-Driver dataset, respectively. These scores surpass those obtained by similar lightweight models. Furthermore, ablation experiments reveal that deep and dilated convolution significantly enhance model performance. In addition, the CoViT model demonstrates its applicability to real-time driving behavior detection tasks, with a parametric count of just 1.24M — a reduction of 2.67M compared to MobileNetV3 — and an online inference Frames Per Second (FPS) of 159.13.

An accurate prediction of a driver’s head pose in the in-vehicle information system (IVIS) tasks is essential for both distraction warning and vehicle takeover rule-making in human-machine co-driving. In this study, a real-world experiment is conducted to collect data on driver behavior and vehicle state during the IVIS tasks under different road conditions. An innovative scheme is proposed for multi-step time-series prediction of a driver’s head pose. The proposed scheme consists of two main parts: a data processing module and a model module. Furthermore, a combination of a machine learning-based method and a geometric method is used to estimate a driver’s head pose, and the MediaPipe model is used to extract the hand data. In the model module, a convolutional neural network (CNN), which can automatically capture short-term local dependencies in the head pose data, and a bidirectional long short-term memory (BiLSTM), which can capture long-term macro dependencies, are combined. In addition, an attention mechanism is used to weigh important time periods and variables. Finally, the performance of the proposed hybrid CNN-BiLSTM-attention model in the head pose prediction is validated, and the proposed model is compared with three traditional machine learning-based models and the CNN-BiLSTM model. The comparison results show that the proposed model outperforms the other models in the prediction steps and can provide reliable driver’s head pose data for distraction warning and takeover rules.

This study is conducted on a real live highway to investigate the driver’s performance in estimating the speed and distance of vehicles behind the target lane during lane changes. Data on the participants’ estimated and actual data on the rear car were collected in the experiment. Ridge regression is used to analyze the effects of both the driver’s features, as well as the relative and absolute motion characteristics between the target vehicle and the subject vehicle, on the driver’s estimation outcomes. Finally, a mixed algorithm of extreme gradient boosting (XGBoost) and deep neural network (DNN) was proposed in this paper for establishing driver’s speed estimation and distance prediction models. Compared with other machine learning models, the XGBoost-DNN prediction model performs more accurate prediction performance in both classification scenarios. It is worth mentioning that the XGBoost-DNN mixed model exhibits a prediction accuracy approximately two percentage points higher than that of the XGBoost model. In the two-classification scenarios, the accuracy estimations of XGBoost-DNN speed and distance prediction models are 91.03% and 92.46%, respectively. In the three-classification scenarios, the accuracy estimations of XGBoost-DNN speed and distance prediction models are 87.18% and 87.59%, respectively. This study can provide a theoretical basis for the development of warning rules for lane-change warning systems as well as insights for understanding lane-change decision failures.

In recent years, the full-touch human–machine interface (HMI) mode has been widely used in vehicles built by Tesla. This interaction mode replaces the conventional physical interaction modality with a screen, and it has a good sense of technological experience. However, it is unclear whether this mode will make the driver's lateral control more challenging than the conventional mode (CM). To investigate this issue, two most common secondary tasks were designed: dialing and navigation entry tasks and real-world road experiments were conducted using two instrumented vehicles. The vehicle operating parameters and the driver manual data were collected in different modes, respectively. Interestingly, the opposite results were found regarding the effect of the full-touch mode (FTM) on the driver's lateral control ability in different secondary tasks. Compared with the CM, the lateral control ability decreased less during the dialing task relative to the baseline driving in the FTM, while the lateral control ability decreased more in the FTM during the navigation entry task. In addition, drivers’ lateral control decreased further as task difficulty and driving speed increased regardless of mode. This study provides a theoretical basis for the development of laws and regulations regarding full-touch HMI mode.

The difference between the desired trajectory of the drivers and the lane change trajectory of autonomous vehicles is one of the crucial factors that limit their development. This paper proposes a lane change trajectory prediction framework, referred to as LCT-DPP, which incorporates driver personality parameters to improve the acceptance of the lane change trajectories of autonomous vehicles. In order to identify the lane-changing styles of the drivers, a clustering algorithm Autoencoder (AE) -Gaussian Mixture Model (GMM) is first proposed to cluster the historical lane-changing trajectories and vehicle interaction parameters of the vehicles and classify the lane-changing styles of the driver into aggressive, normal, and conservative according to the clustering results. The encoder module and the fully connected layer are then used to predict the lane change style of the driver, and the output is a vector holding the lane change style features of the driver. Finally, the encoder-decoder model is developed for lane change trajectory prediction by fusing lane change style feature vectors and time series features and using them as inputs to the model. The proposed model is then validated through experiments. The obtained results show that the root mean square errors (RMSEs) of the TCN-LSTM-Attention based encoder-decoder model are respectively 0.312, 0.568, and 0.734 m for the time intervals of 1, 2, and 3 s, which indicates a very high prediction accuracy.