In the rapidly evolving field of Intelligent Transportation Systems, users’ low acceptance caused by psychological barriers has emerged as a significant obstacle to realizing the vision of a safe, efficient, and smooth mobility experience. Building on the foundation of technology acceptance research, this study introduces and validates the concept of situational acceptance within the context of automated driving, and establishes a two-dimensional model of acceptance based on a driving simulator study. This model comprises two dimensions: positivity (the tendency to accept or reject) and firmness (the strength of that tendency). Each dimension showed correlations with objective indicators, including decision-making timing, gaze fixation, pupil diameter, and eyelid opening, and these relationships were further explained by psychological theories. Furthermore, multiple machine learning models demonstrated strong classification performance on the feature set (four-class accuracy up to 0.6379 and binary accuracy up to 0.8608), further confirming the utility of the selected features. The incorporation of the two-dimensional model maintained or even enhanced classification accuracy while substantially reducing computational cost, with FLOPs decreased by up to 96%, laying the groundwork for developing a real-time, objective acceptance estimation method. Additionally, the study provides implications to enhance acceptance in interface transparency, motivational mechanisms, and adaptive personalization, offering actionable insights for developing real-time, adaptive in-vehicle systems.

This study addresses the gap in understanding drivers’ eye movement behaviors during automated lane changes and explores the application of driver monitoring in personalizing highly automated driving systems. Through a driving simulator experiment, this study thoroughly investigated the relationship between participants’ eye movement behaviors and subjective perceptions. 37 participants each experienced 48 different automated lane changes in the experiment. Their subjective feedback on situational trust and system aggressiveness was collected through questionnaires, and eye movements related to gaze, blink, saccade, fixation, pupil diameter, and eyelid opening were monitored. The analysis identified 25 significant eye movement features. The experimental results show that lower situational trust during automated lane changes correlated with increased attention to side mirrors, decreased attention to the front vehicle in the original lane, reduced blink frequency and suppressed saccade amplitude which increased the duration of effective visual information acquisition, smaller pupil diameter and greater fluctuations indicating increased cognitive load. The extracted features proved effective in building predictive models for drivers’ subjective evaluations. This study provides practical recommendations for selecting eye movement features and implementing personalized automated lane-change systems through eye movement monitoring. Future research will focus on expanding the dataset, improving predictive model performance, and exploring the application of these models.

Accurately inferring the driving intentions of neighboring vehicles is a critical challenge for autonomous vehicles (AVs) when handling complex interactions in urban environments. These interactions are complicated by diverse driving styles, which AVs often struggle to interpret, leading to overly cautious driving strategies. In this work, a trajectory prediction and planning framework, StyleFormer, is proposed which considers vehicles’ driving styles. The model classifies short-term driving styles using an unsupervised method and employs a vectorized representation to integrate map features, agent states, and driving styles. A Transformer-based attention mechanism is used to model interactions and intentions, enabling joint prediction of future trajectories for surrounding vehicles and multimodal trajectory generation for AVs. StyleFormer further adapts planned trajectories to different driving style pReferences and leverages diffusion-based optimization to enhance safety and feasibility. It effectively models multi-modal driving behaviors and ensures trajectory quality without relying on rule-based post-processing. Validated on the Argoverse 1 and nuScenes datasets, StyleFormer achieves superior performance in open-loop trajectory prediction and planning, and reaches a 96.33% success rate in closed-loop planning. The results demonstrate enhanced prediction accuracy, efficient and safe planning in complex scenarios, and strong generalization across diverse urban environments.

To practically apply level 2 automated driving in complicated conditions including intersections where events that require manual interventions occur frequently, it is necessary to consider the influences of provided traffic information on driver behaviors. This study performed driving simulator experiments to evaluate the effects of two kinds of information on driver behaviors while interacting with oncoming vehicles at intersections, of which static information informed drivers of the approaching intersections, and sensor information offered the real-time object detection results of the system to drivers. It was observed that the distances to oncoming vehicles at takeover decreased when only the static or sensor information was displayed, and it could be improved when both kinds of information were provided, compared to the condition when no information was offered. Meanwhile, drivers’ feeling of safety significantly increased with the presentation of both kinds of information. The results indicated that the combination of the static and sensor information might improve drivers’ feeling of safety during level 2 automated driving, without delaying drivers’ intervention.

As drivers are frequently distracted during conditionally automated driving, how and when to issue takeover requests when it is necessary have become concerns. A good practice is to predict takeover performance of drivers in real time, so that appropriate measures can be taken in corresponding to the predicted results. However, that is difficult in that a lot of factors need to be taken into consideration, especially human-related factors. Among all the factors researched, impact of personality on takeover performance has rarely been researched, which is essential for building a personalized prediction model that involves human drivers. To explore the effect of personality on takeover performance, a driving simulator experiment involving 48 participants and 6 critical takeover scenarios was conducted in this study. The big five personality test was utilized for assessing personality of the participants. Overall, results revealed that different personality traits seemed to affect takeover performance in different aspects, such that extraversion and openness mainly affect takeover time, and neuroticism and agreeableness mainly affect longitudinal and lateral performance, respectively. Moreover, effects of personality are most significant when drivers have gained certain levels of situation awareness. Finally, regarding using of turn signals shortly after takeover requests, it was found that turn signal missing rates were positively related with neuroticism, openness and conscientiousness, respectively, and negatively quadratically related with agreeableness. These results might shed light on the factors we need to take into consideration when considering building a personalized prediction model to predict different aspects of takeover performance of drivers.

It is becoming a common scene to observe the usages of level 2 automated driving in our daily life. To ensure driving safety while using the level 2 automated driving systems in various traffic conditions, it is an essential issue to clarify the influences of the level 2 automated driving on driver behaviors. Previous studies normally focused on drivers’ reactions to emergency events while using level 2 automated driving. However, it is still unclear that how will drivers interact with the level 2 automated driving systems during the periods when no emergency event occurs. Therefore, a driving simulator experiment was performed, and the differences in driver behaviors, especially eye-gaze behaviors, under level 2 automated driving and manual driving were analyzed. Meanwhile, to simulate non-driving related tasks that may occur in real driving environment, a visual task, Surrogate Reference Task (SuRT), was applied in the experiment. It was observed that the percentage of gaze that fell within the road center area and speedometer significantly decreased, and the gaze time to the left and right mirrors, and the SuRT display significantly increased during level 2 automated driving. Meanwhile, the eyelid closure time were significant longer and the subjective evaluation scores of attention to front and surroundings were significant lower while applying level 2 automated driving. The results indicated that drivers’ attention levels, especially for the front areas, might be significantly reduced during level 2 automated driving, compared to that of manual driving.

The lane-change decision-making module of automated and connected vehicles (ACVs) is one of the most crucial and challenging issues to be addressed. Motivated by human beings' underlying driving paradigm and the convolutional neural network's (CNN) dramatic capability of extracting features and learning strategies, this article proposes a CNN-based lane-change decision-making method via the dynamic motion image representation. Human drivers take proper driving maneuvers after they subconsciously construct the dynamic traffic scene representation in their brains, so this study first proposes the dynamic motion image representation method to reveal informative traffic situations in the motion-sensitive area (MSA), which provides a full view of surrounding cars. Then, this article develops a CNN model to extract the underlying features and learn driving policies from labeled datasets of MSA motion images. Besides, a safety-constrained layer is added to avoid vehicle collisions. We build a simulation platform based on the simulation of urban mobility (SUMO) to collect traffic datasets and test our proposed method. In addition, real-world traffic datasets are also involved to further investigate the proposed method's performance. The rule-based strategy and reinforcement learning (RL)-based method are used to compare with our approach. All results demonstrate that the proposed method performs lane-change decision-making much better than prevailing methods, which suggests our scheme has huge potential to accelerate the deployment of ACVs and is worth further study.

Automated driving is transforming the nature of passenger interaction and user experience. In highly automated vehicles, establishing passengers' trust is essential for interactions and key to achieving a positive Quality of Experience (QoE). Improving system transparency is widely employed among the various methods for calibrating trust. However, increasing transparency can also harm QoE by increasing passenger workload. This research examines this trade-off through an experiment conducted in a driving simulation environment. Our findings reveal a positive correlation between system transparency and trust but a non-monotonic correlation with the workload. This suggests that there may be better options than maximizing system transparency for QoE design, and workload management should be considered to optimize system transparency. Based on our design practice and verification, we provide suggestions for improving system transparency, including information integration, synergy, feedback, and auxiliary prediction.

Thorough performance evaluation of automated vehicles (AVs) is an essential prerequisite for AVs' release and deployment. The challenges posed by dynamics performance appraisal of AVs are centered around the complexity of chassis dynamics, performance diversity, and lack of unified quantitative metrics. Therefore, this article proposes a novel quantitative evaluation metric for AVs' chassis-domain performance. We reveal mathematically explicit chassis steady boundaries of various vehicle maneuvers based on the modeling of chassis-domain dynamics and vehicle spatiotemporal signal analysis for safety-critical AVs. By defining and analyzing the multiperformance appraisal problem, this article gives mathematically prerequisites for evaluation metrics. Then, a rigorous metric is developed to quantify AVs' safety and comfort performance comprehensively. Wherein, the steady boundaries are leveraged to the metric normalization. We demonstrate the effectiveness of the proposed quantitative evaluation methodology in various scenarios. Test results illustrate that the proposed method provides a quantitative way to test AVs' integrated dynamics performance.

It is an essential issue for autonomous vehicles to keep driving safety while sharing spaces with other road users, especially moving pedestrians. An ideal trajectory planning of autonomous vehicles should be collision-free and feasible for the vehicles to execute. Previous efforts on trajectory planning mainly focused on traditional roads, and the research into pedestrian-vehicle interaction in shared spaces were still insufficient. This study proposed a prediction-planning collaboration method for autonomous vehicles to avoid collisions with moving pedestrians by predicting their motions, and generate feasible trajectories based on Frenet coordinate in shared spaces. Pre-crash scenarios were designed based on reported crashes, and simulations were performed to evaluate the collision avoidance performances of the proposed method. Meanwhile, validations with real world dataset were conducted to verify the practicability of the proposed framework in real driving environment. The results indicated that the proposed prediction-planning collaboration method could effectively predict the motions of moving pedestrians and ensure the safe interactions between autonomous vehicles and pedestrians in shared spaces.

Driving maneuver decision-making is critical to the development and mass deployment of automated vehicles (AVs). The prevailing approaches are stuck with either specific optimization objectives or separate maneuver planning. Inspired by the inherent driving mechanism of human beings, we develop a comprehensive deep learning scheme that extracts and establishes informative image-based representation of dynamic traffic flows and then learns driving strategies from collected driving datasets based on convolutional neural networks (CNNs). Our scheme can effectively capture comprehensive and dynamic features of traffic flows surrounding the ego car and construct dynamic motion images through processing the spatio-temporal signals of all neighbor cars. These constructed virtual images cover all informative dynamic states of the neighbor cars, located in the maneuver-critical area, defined as the motion-sensitive area (MSA). Then, sequential spatio-temporal images together with labeled driving behaviors are fed into our proposed CNN model. The network can extract underlying motion patterns and learn proper driving behaviors, including both the lateral maneuver and longitudinal speed. We demonstrate the effectiveness of the proposed scheme in a typical highway scenario. Results suggest that the proposed scheme merits further investigation to promote the launch of AVs.

Fast and accurate long-term trajectory prediction of surrounding vehicles (SVs) is critical to autonomous driving systems. In high-density traffic flows, strongly correlated vehicle behaviors require considering the interactions among multiple SVs when predicting their future trajectories. However, existing interactive prediction methods, most based on Long Short-Term Memory (LSTM), are suffering from slow prediction because they analyze SVs one by one and analyze trajectory sequence node by node. This paper presents a fast interactive trajectory prediction method called Structural Transformer which learns both spatial and temporal dependencies among multiple SVs in parallel. Specifically, our model first removes the internal states and loops of LSTM and replaces with a weighted self-reference mapping to realize parallel computation. Then, it embeds the relative spatial information of multiple SVs into trajectory states and reorganizes the self-reference mapping with neighbor-only interaction masks to achieve interactive prediction. Results on the NGSIM dataset show satisfyingly speed and accuracy performance on long-term trajectory prediction of multiple SVs. The longitudinal and lateral errors are reduced to 2.67m and 0.25m over 5s time horizon. The computational time of each step is only 12ms on a 2080ti GPU, which is over 4 times faster than the Structural LSTM.

It is an essential task to promote communication between vehicles and their surrounding road users to avoid potential traffic accidents. With the development of lighting technologies, it is becoming possible for vehicles to display more detailed information to other road users in an easier way. An exterior lighting system was, therefore, proposed to display warning signals from parked vehicles on the road surface to call for the attentions of cyclists. By displaying warning signals in either a flashing or an animation way, it is expected that the cyclists can be informed of the door opening and reversing actions of the parked vehicles, even when their focus is not on the vehicles. However, it is still unclear that how the cyclists will react to the displayed signals. This study, therefore, performed a field experiment with 12 participants to investigate the influences of the proposed system on the behaviors of cyclists. It was observed that the median values of the avoiding distance to the parked vehicles could be improved to the recommended safe distance when the exterior lighting system was applied, especially for the participants without driving licenses. Meanwhile, the stress of the cyclists could be reduced, and the awareness to the actions of parked vehicles could be improved while using the system.

In this article, the authors present a novel method to learn the personalized tactic of discretionary lane-change initiation for fully autonomous vehicles through human-computer interactions. Instead of learning from human-driving demonstrations, a reinforcement learning technique is employed to learn how to initiate lane changes from traffic context, the action of a self-driving vehicle, and in-vehicle user's feedback. The proposed offline algorithm rewards the action-selection strategy when the user gives positive feedback and penalizes it when negative feedback. Also, a multi-dimensional driving scenario is considered to represent a more realistic lane-change trade-off. The results show that the lane-change initiation model obtained by this method can reproduce the personal lane-change tactic, and the performance of the customized models (average accuracy 86.1%) is much better than that of the non-customized models (average accuracy 75.7%). This method allows continuous improvement of customization for users during fully autonomous driving even without human-driving experience, which will significantly enhance the user acceptance of high-level autonomy of self-driving vehicles.

A lot of research has been done to model and predict a driver's behaviors to improve driving safety. Inferring lane change maneuver can be a critical one among them. However, the lane change prediction problem is generally treated as a classification task in which the labels represent the probability of whether the driver will make a lane change in the upcoming few seconds. In our work, we formulate this problem as a regression task. The process of lane change behavior is analyzed to build a Gated Recurrent Units (GRU) network for predicting two time points during lane change behavior: a) When the driver will shift lane. b) When the lane change will be completed. We make a comparison of Long Short Term Memory (LSTM) network and Support Vector Machine (SVM) regression performance to show that our method can give a more precise prediction time. This work can be used to improve the safety performance of driver assistance systems and help other traffic participants having a safer environment.

Dynamic driving task (DDT) fallback turns to be an essential part in level 3 or higher driving automation systems, which is responsible to either perform the DDT or achieve a minimal risk condition after encountering automated driving system (ADS) failure. As a typical ADS failure, sensor failure can prevent ADS from performing on-road driving safely, and thus a minimal risk condition can be achieved when the failed vehicle stops away from the active lane. Therefore, this paper considers a level 4 ADS-dedicated vehicle encountering front sensor failure in highway traffic. The proposed fallback approach is designed to avoid potential collision with surrounding vehicles while bringing the vehicle to a stop on the designated parking zone, thus achieve a minimal risk condition. Safety constraint is proposed based on the assumption that the failed vehicle is remarkable by surrounding vehicles, and enforced using model predictive control. As a result, simulation is conducted in Carsim/Simulink environment.

An in-vehicle traffic light system was proposed to assist drivers at intersections, by displaying traffic light inside vehicles based on vehicular communication. Driving simulator experiments have demonstrated that the system can be an effective method to provide assistance for drivers. However, previous studies assumed that all the vehicles were equipped with vehicular communication devices and the proposed in-vehicle traffic light, which is still impossible in the actual driving environment. Therefore, it is necessary to evaluate the availability of the system in a driving condition when both in-vehicle traffic light equipped and unequipped vehicles exist. This study implemented the in-vehicle traffic light system with a real electric vehicle at a signalized intersection, based on V2I communication. An experiment involving 12 participants and two vehicles were performed, to analyze the influences on driver behaviors while applying the in-vehicle traffic light in a partial deployment environment. It was observed that the application of in-vehicle traffic light in a preceding vehicle could significantly reduce the maximum deceleration of its following vehicle, even when the following vehicle was unequipped with the system.

Highly automated driving tremendously reduces workload of drivers. However, the drivers may lose their visual attention to road ahead due to the out-of-the-loop problem. Haptic guidance has been developed to reduce drivers' workload while keeping the drivers in the control loop. The haptic guidance system continuously provides assistant torques on the steering wheel so that both the driver and the system contribute to the steering input. This paper focuses on analyzing drivers' visual attention to road ahead and response to critical events when driving with different levels of haptic guidance. A simulator experiment with five participants was conducted. There were five driving conditions: hands free, hands ready, strong guidance, weak guidance, and manual driving. Autopilot driving was realized by the highest level of haptic guidance in the conditions of hands free and hands ready. Results indicate that percent road center was significantly higher in the condition of weak guidance compared to hands ready. Root mean square of steering torque was significantly lower in the condition of weak guidance compared to manual driving. Moreover, the drivers' response to the critical event indicates that lane change maneuver was smooth in the conditions of weak guidance and strong guidance.

Most of intersections are without traffic signals to help drivers safely pass through intersections. Nowadays, it becomes possible to transfer traffic information of unsignalized intersections to drivers by application of communication technologies. Therefore, this study concentrated on in-vehicle traffic lights for assisting drivers to cross unsignalized intersections. Dependent on different traffic situations, unsignalized intersections can be classified as priority-controlled and non-priority-controlled intersections. Consistently, in-vehicle traffic lights were elaborated and provided to drivers in different unsignalized intersections, considering gap acceptance to ensure driving safety. A driving simulator experiment involving nine participants was performed to evaluate the effectiveness of the proposed system. The experimental results indicated that post-encroachment time was significantly improved when in-vehicle traffic lights were applied for crossing unsignalized intersections.