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.

In urban environments, road closures due to construction, maintenance, accidents, or emergency situations pose significant challenges to traffic flow and safety. The cooperative lane-changing (CLC) is a promising solution. Traditional rule-based CLC models often fall short in addressing the complexities introduced by sudden lane reductions and diversions. Therefore, this paper proposes a vehicle-to-everything (V2X)-aided multi-agent CLC (MA-CLC) model, which is tailor-made for road closure scenarios. By leveraging V2X, the connected and autonomous vehicle (CAV) and human driven vehicle (HDV) agents can share information about speeds, distances, and trajectories, which will be used as part of the state space for training. To make the HDV more humanlike, we customize a human-imitating reward function for HDV agents and implement CLC experiments with human expert drivers (HEDs). The results show that the safety and efficiency performance of the proposed MA-CLC model is respectively 15% and 26% higher than other benchmarks on average.

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.

One challenge of haptic guidance steering system is to be both efficient on reducing lane departure risk and as comfort as possible for driver acceptance. This paper focuses on design and evaluation of a comfort-oriented haptic guidance steering system via deep reinforcement learning for lane keeping purpose by numerical simulation. The design is based on a Deep Q Network to learn an end-to-end mapping from environmental observation and driver input to agent action values, with task reward as the only form of supervision. The evaluation is based on lane keeping accuracy measured by mean absolute lateral error from the centerline of lane and steering control activity measured by mean absolute steering wheel angle. By comparison, other two types of haptic guidance steering, namely continuous haptic guidance with constant gain and threshold-based haptic guidance are addressed. The results indicate that haptic guidance via reinforcement learning has the best performance on reducing steering control activity while remaining a high lane keeping accuracy. Furthermore, the proposed system shows its capability of being adaptive to individualized drivers. This paper suggests the potential of using deep reinforcement learning to design haptic guidance steering system for improving individualized driver safety and comfort.

In this study, based on the measurement of brain activity and the change of accelerator and brake stroke, we tried to grasp the interaction between the driver's reaction and the driver's following behavior at the time when driver watched Variable Message Sign on actual car driving. Specifically, using fNIRS, we analyzed same driver's brain activity and driving behavior during actual car driving, and then we evaluated the interaction of both items. As a result, we confirmed that parietal association cortex and prefrontal area activated in the case of driving with recognition and judgment for the information which a driver collected from the environment during driving. Then it was suggested that it was needed to expand parietal association cortex in order to measure the brain activity. Furthermore, it was suggested that both on and off accelerator stroke were connected with the activity of prefrontal area. As a result, it was suggested that it was valid to confirm the driver's reaction at every steps, such as “recognition”, “judgment”, “behavior”, by means of being approached from a neuroscience.

This paper presents a driver model in shared control that focuses on driver interaction with haptic guidance steering for a lane-following task. A weight parameter in the driver model was developed to describe driver interaction and reliance on haptic guidance steering. A driving simulator experiment with 5 participants was conducted to identify the model parameters. One experimental condition was driving with the haptic guidance steering system, and another condition of manual driving was conducted as a comparison. The identification results showed the relationship between driver model parameters and characteristics of driver behavior, including the driver interaction with haptic guidance steering and the steering effort. Taking the degree of driver reliance on haptic guidance steering into account improved the fitness of modeling driver steering behavior. The proposed driver model in shared control driving was shown to be appropriate, as the model matched the driver's input torque with a fitness of 68% on average.

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.

This paper focuses on the effect of haptic guidance steering on driver lane following performance by simulation study and, in particular, different degrees of driver reliance on the assistance system are addressed. Driver interaction and reliance on the assistance system is represented by two parameters in a driver model: target steering angle to torque gain and neuromuscular reaction gain for haptic feedback. The haptic guidance system is designed based on a two-point visual model and applies a proportional-derivative control theory. The lane following performance with different degrees of driver reliance on the assistance system is compared and evaluated using three different sets of driver parameters, and a manual (unassisted) driving condition is conducted as comparison. Moreover, lane following performance is also investigated in the case of a system failure. The results indicate that the haptic guidance system is effective on improving lane following performance, and the performance is sensitive to the degree of driver reliance on the assistance system, especially in the case of a system failure. The cost-benefit analysis suggests that the driver determines the degree of reliance on the assistance system by balancing the improvement of lane following performance and maintenance of own driving strategy.

Visual feedback from the road ahead is required for steering a car. When visual feedback is limited or only partial road is visible, driver steering performance declines. To solve this problem, haptic feedback is expected to assist drivers by providing guidance torque on the steering wheel. This paper focuses on the effect of haptic guidance on driver steering performance during curve negotiation when visual feedback is limited. Twelve subjects participated in the experiment conducted in a high-fidelity driving simulator. Levels of haptic guidance were none, weak, and strong, and levels of visual feedback were whole, near, medium and far. The steering performance was assessed by drivers' turning maneuver when approaching and leaving curves, and time-to-lane crossing during curves. Results indicate that mean value of time-to-lane crossing decreased due to the implementation of strong haptic guidance when visual feedback was limited. The start point of turning maneuver was earlier resulting from the implementation of strong haptic guidance under the condition of visual feedback from near segment. In conclusion, the drivers tended to rely on haptic guidance to achieve better steering performance when visual feedback was limited.

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.

Prolonged driving on monotonous roads often leads to a reduction in task load that causes drivers passive fatigue. Passive fatigue results in loss of driver alertness and is detrimental to driver safety. This paper focuses on the effect of a haptic guidance steering system on improving behaviors of passively fatigued drivers. By continuously exerting active torque on a steering wheel, the haptic system guides drivers to follow the centerline of a lane; meanwhile, the drivers sense the torque and interact with it while operating the steering wheel. An experiment was conducted with 12 healthy participants in a high-fidelity driving simulator. A monotonous driving course was designed, and vehicle speed was fixed in order to induce drivers' passive fatigue. A treatment session was arranged with the haptic guidance steering system, and a control session was conducted as a comparison. Driving performance, assessed by standard deviation of lane position, mean absolute lateral error, and time-to-lane crossing, was significantly improved when haptic guidance was activated. Results of physiological measures, including heart rate variability and percentage of eye closure, revealed that passively fatigued drivers were aroused when they were aware of the active torque on the steering wheel. In conclusion, the activation of haptic guidance can be regarded as an effective countermeasure for the passively fatigued drivers who have performed a prolonged monotonous driving task.

Haptic steering technology has been developed to support drivers for more accurate and reliable vehicular control. This paper investigates the evaluation of steering performance when a haptic guidance steering system is implemented for passive fatigued drivers. The haptic system continuously produces torque on the steering wheel to inform drivers about the lateral lane deviations; consequently, drivers are aware of the active torque and then contribute to the steering task by interacting with the system. An experiment with 12 participants was conducted in a high-fidelity driving simulator. In a within-subject counterbalanced design, one session included implement of haptic system and the other excluded haptic system. A monotonous long-period driving course was designed to induce drivers' passive fatigue. Electromyography signals of the brachioradialis muscles were measured to examine grip strength of a driver holding a steering wheel. Results show that the risk of lane departure was significantly reduced when the haptic system was activated. In addition, grip strength of the drivers decreased when cooperating with the haptic system.

With the advent of global positioning system technology, smart phones are used as portable navigation systems. Guidelines that ensure driving safety while using conventional on-board navigation systems have already been published but do not extend to portable navigation systems; therefore, this study focused on the analysis of the eye-gaze tracking of drivers interacting with portable navigation systems in an urban area. Combinations of different display sizes and positions of portable navigation systems were adopted by 20 participants in a driving simulator experiment. An expectation maximum algorithm was proposed to classify the measured eye-gaze points; furthermore, three measures of glance frequency, glance time, and total glance time as a percentage were calculated. The results indicated that the convenient display position with a small visual angle can provide a significantly shorter glance time but a significantly higher glance frequency; however, the small-size display will bring on significantly longer glance time that may result in the increasing of visual distraction for drivers. The small-size portable display received significantly lower scores for subjective evaluation of acceptability and fatigue; moreover, the small-size portable display on the conventional built-in position received significantly lower subjective evaluation scores than that of the big-size one on the upper side of the dashboard. In addition, it indicated an increased risk of rear-end collision that the proportion of time that the time-to-collision was less than 1 s was significantly shorter for the portable navigation than that of traditional on-board one.

This paper proposes a road surface recognition system based on a “laser radar” (LIDER), which is used to detect a lane markings for application to an automatic platooning system for trucks. To ensure the safety of automatic driving, there is a need to recognize the road surface conditions (dry, wet, etc.). This system proposes an integrated system that is not only capable of recognizing lane markings but also monitors the road surface using a laser radar scanning system. Our road surface recognition method relies on the multiple reflection intensities of laser radar and a machine learning algorithm. By using multiple reflection intensities, the recognition rate is improved. Moreover, to improve the recognition rate, an additional feature variable, called the “roughness index,” is proposed. In this paper, the concept of a road surface recognition system is proposed and six road surface conditions (dry old asphalt, moist old asphalt, flooded old asphalt, dry new asphalt, moist new asphalt, and flooded new asphalt) are recognized by the proposed algorithm. The quality of the road surface recognition is examined through comparison with long-term measurement data. The proposed method exhibits a high level of road surface recognition performance.

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.

Dynamic of driver arm plays an important role in the understanding of driver intentions. Combined with mechanical driving variables and human-related parameters, it allows building a road safety system based on driving intensions and could possibly provide corrections to driving mistakes. Mechanical arm admittance is a driving related parameter which represents the reaction of driver facing a steering perturbation. It is used to state driver condition. If comparison of two mechanical arm admittance amplitudes permits knowing the difference of driver condition on the two trials, monitoring a driver in real-time provides only one mechanical arm admittance data. Then a calibration is needed to know the condition of the driver. In this study, a method for scaling mechanical arm admittance is proposed. Moreover, results indicate that this calibration has to be done individually.

Most car accidents on roads are the consequence of human mistakes. In order to improve the driving safety, current car devices should provide useful information to the driver on his/her environment and help the driver via steering wheel, dashboard signals or audio announcement when assistance is needed. An important factor which disturbs driving behavior and can lead to crash is driver distraction. It has been accurately proved that driver subjected to distracting tasks leads to a massive increase in road accident rate. The main problem lies in the lack of driving focus, which is caught by other tasks. As a result building safety car devices requires knowing the influence of distraction on driving performances and mechanical admittance. This paper aims to investigate on the correlation between the car trajectory deviation, speed bearing and mechanical arm admittance under the influence of distracting tasks. Mechanical arm admittance, to a certain extent, represents driver aim because it investigates on the relation between the perturbations, the driver reaction and the corresponding answer. Experiment results show that in 70% of investigated cases, driver distraction either decrease resistance to steering wheel perturbations or lead to poorer driving performances.

It becomes popular to use smart phones as a car navigation system. Conventional navigation systems were already designed following the published guidelines to ensure driving safety; however, guidelines for the smart phone navigation system are still undeveloped. To assess the safety of these devices, the car navigation systems are integrated into a driving simulator. The experiments are carried out by changing the size and the position of the display. Gazing points of the drivers are measured when they are driving on the driving simulator following the instruction of the car navigation systems as well as obtaining questionnaires. The results indicate the frequency of eye movement increases when the display is installed close to the driver's eye, while total time gazing the display does not change significantly in all the conditions.

In developing automatic platooning of trucks as an energy-saving technology, the reliable driving of the platooned trucks is a primary objective for public implementation and future applications. At the same time, there is also an emergency requirement to ensure the safety of the driving experiment in the automatic platooning of trucks, including the conditions of a system failure. This paper presents a detailed experimental study on emergency avoidance braking for the automatic platooning of trucks using a driving simulator (DS) and an actual-vehicle experiment. In addition, a modification on the braking capability of the trucks of a platoon was applied for safety control. Therefore, human drivers can brake without risking a rear-end collision, in the case of an emergency for a failure in automatic platooning. Initially, an experimental platform was built to reproduce the automatic platooning of trucks in an advanced DS system. Assuming system failure and the emergency deceleration of the preceding truck without warning, the behavior of the driver in the following truck was studied in terms of emergency avoidance of a collision. In particular, with different settings for the mean maximum decelerations of the brake system of the following truck, the stopping gap distances and driver reaction times were analyzed in the driving experiment using the advanced DS and an actual vehicle. The experimental results indicated that emergency braking is an effective method for avoiding a rear-end collision when there is a system failure in the automatic platooning, resulting in the mean maximum deceleration for the following truck being higher than that for the preceding truck.

Human factors are becoming one of the most important factors that are considered for automobile design and test. However, the ride comfort of a passenger or driver is mainly dependent on a subjective assessment by a test driver or questionnaire investigation, and, therefore, a quantitative evaluation of the ride comfort is being pursued as one of the research goals in the automobile industry. In this paper, actual-vehicle and driving simulator (DS) experiments were carried out to evaluate the sternocleidomastoid (SCM) muscle activity of a passenger in response to a car's lateral acceleration while slalom driving. Interestingly, the SCM muscle of the passenger on the side opposite the direction of the car's lateral acceleration contracts to keep the head stable against the body shaking. The electromyography (EMG) signal of the SCM muscle in a modified car was significantly lower than in a normal car, because the 1-10 Hz low-frequency vibrations of the body frame of the modified car during the slalom driving were decreased through improvements of the rigidity of car's body frame. A passenger feels more discomfort when the EMG signal of the SCM muscle increases, and less as the signal decreases. The DS experiment, with the addition of more experimental conditions, arrived at the same conclusion, which testified that the DS is a powerful tool with which to evaluate passenger discomfort. In conclusion, the EMG of the SCM muscle can be considered as an objective and effective method with which to quantify the effect of vehicle properties on human discomfort in both actual-vehicle and DS experiments for slalom driving.

Sternocleidomastoid (SCM) muscle activity to keep the head stable in slalom driving is analyzed by surface electromyography (sEMG). Using sEMG signals of SCM muscles, this study evaluates a passenger's sensitivity to different vehicle dynamic characteristics, especially in terms of lateral and roll vabrations. In actual slalom driving, it is found that the sEMG signal is more active when the amplitude of the relative acceleration of the car body is higher. In a further experiment, a universal driving simulator is used to simulate the actual slalom driving for two kinds of slalom driving. Meanwhicle, 1–10 Hz white noise is input into the lateral and roll directions of the vehicle motion for the two types of slalom driving. The result shows that root-mean-square values of the sEMG signal of the SCM muscle in slalom driving with added lateral and roll vibrations are significantly higher than those in the normal slalom driving. Importantly, the study indicates that the amplitude of the sEMG is sensitive to lateral and roall vibrations of the vehicle, which may result in passenger discomfort.