Autonomous technologies have developed incrementally, especially in the car industry. Countries worldwide are giving impetus to developing automated driving (AD) and autonomous vehicles (AVs). China ...is used as an example of an innovative development model for autonomous driving that has significant practical implications for AD's development in the global context. Germany also keeps its role as a global hub for the AVs industry, and it has released the most advanced AD laws. This research conducted a thorough comparison of related policies, regulations, and industries in China and Germany in an effort to support the development of AD in China. Further, AD's advantages, disadvantages, opportunities, and threats in China and Germany are discussed. It sought to identify any shortages of AD in China and find an efficient way to enhance AD cooperation between China and Germany. Based on the dissimilarity of the current status and the disparity in policies, a series of countermeasures and suggestions for the development of the Chinese AD are presented in this study.
The current study investigated the effects of secondary task relevance on measures of vigilance decrement in a Level 2 semi-autonomous driving simulation. Past research has demonstrated that over the ...course of a vigilance task, vigilance (or sustained attention) decreases which leads to worse performance on measures like reaction time and accuracy. This phenomenon is known as the vigilance decrement. However, the underlying cause of the vigilance decrement is under debate. Secondary tasks can not only shed light on this debate but they might also potentially help mitigate this vigilance decrement if designed and implemented properly. Therefore the current research used secondary tasks to manipulate task demand and task engagement to further investigate the cause of the vigilance decrement.
In this paper, an automated driving system (ADS) data acquisition and analytics platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected automated vehicle (CAV) ...cooperative perception are presented. This platform presents a holistic pipeline from the raw advanced sensory data collection to data processing, which is capable of processing the sensor data from multi-CAVs and extracting the objects’ Identity (ID) number, position, speed, and orientation information in the map and Frenet coordinates. First, the ADS data acquisition and analytics platform are presented. Specifically, the experimental CAVs platform and sensor configuration are shown, and the processing software, including a deep-learning-based object detection algorithm using LiDAR information, a late fusion scheme to leverage cooperative perception to fuse the detected objects from multi-CAVs, and a multi-object tracking method is introduced. To further enhance the object detection and tracking results, high-definition maps consisting of point cloud and vector maps are generated and forwarded to a world model to filter out the objects off the road and extract the objects’ coordinates in Frenet coordinates and the lane information. In addition, to refine trajectories from the object tracking algorithms, a post-processing method is proposed. Given the objects’ information from the object detection and tracking and the world model, a Kalman filter and Chi-square test method are applied to reduce the noise and remove the outlier in the trajectories. Aiming at tackling the ID switch issue of the object tracking algorithm, a fuzzy-logic-based approach is proposed to detect the discontinuous trajectories belonging to the same object. Then, a vehicle-kinematics-based trajectory prediction method is used, and a forward–backward-smoothing technique is applied to reconstruct the trajectory between the discontinuous trajectories. Finally, results, including object detection and tracking and a late fusion scheme, are presented, and the improvements by the post-processing algorithm in terms of noise level and outlier removal are discussed, which confirm the functionality and effectiveness of the proposed holistic data collection and processing platform. In another aspect, the extracted objects’ information and generated HD maps can be used for several purposes in the transportation research community and ADS development community: analyzing the interaction between human-driven vehicles and ADS-equipped vehicles, car-following behavior analysis of ADS-equipped vehicles, traffic flow status analysis and modeling, and scenario generation for ADS testing.
•An automated driving system data processing platform using cooperative perception is designed.•This platform leverages sensor data from connected automated vehicles.•Results of cooperative object detection and tracking in a late fusion scheme is presented.•Postprocessing algorithm is proposed to reduce noise level and outlier removal.
•Social influence and initial trust played the most important roles in AV acceptance.•Some personality traits contributed to AV usage intention.•Sensation seekers and those with a higher openness to ...experience had a higher intention to adopt AVs.•Neurotic users were less likely to accept AVs.
Although automated vehicles (AVs) could offer a potentially effective solution to improving road safety, the benefit associated with AVs can be realized only when the public intend to use them. While some efforts have been made to understand why people would use AVs, few of them have investigated the role of social and personal factors in AV acceptance. The present study aimed to fill in this research gap. An AV acceptance model was proposed by extending the Technology Acceptance Model (TAM) with social and personal factors, i.e., initial trust, social influence, and the Big Five personality and sensation seeking traits. The validity of the proposed model was confirmed with a questionnaire survey administrated to 647 drivers in China. Results revealed that at the very beginning of AV commercialization, perception factors (i.e., perceived ease of use and perceived usefulness) from the original TAM showed significant influence on users’ intention to use AVs. But more importantly, it was social influence and initial trust that contributed most to explain whether users would accept AVs or not. Some personality traits also played certain roles in AV usage intention. In particular, sensation seekers and those with a higher openness to experience were more likely to trust AVs and had a higher intention to adopt them. In contrast, neurotic people showed a lower level of trust and were less likely to accept AVs. Practically, these findings suggest that promotion of AVs to influential individuals that could help form good social opinions would have significant downstream effects on AV acceptance at the early state of its marketization.
A Tutorial on 5G NR V2X Communications Garcia, Mario H. Castaneda; Molina-Galan, Alejandro; Boban, Mate ...
IEEE Communications surveys and tutorials,
01/2021, Volume:
23, Issue:
3
Journal Article
Peer reviewed
Open access
The Third Generation Partnership Project (3GPP) has recently published its Release 16 that includes the first Vehicle-to-Everything (V2X) standard based on the 5G New Radio (NR) air interface. 5G NR ...V2X introduces advanced functionalities on top of the 5G NR air interface to support connected and automated driving use cases with stringent requirements. This article presents an in-depth tutorial of the 3GPP Release 16 5G NR V2X standard for V2X communications, with a particular focus on the sidelink, since it is the most significant part of 5G NR V2X. The main part of the paper is an in-depth treatment of the key aspects of 5G NR V2X: the physical layer, the resource allocation, the quality of service management, the enhancements introduced to the Uu interface and the mobility management for V2N (Vehicle to Network) communications, as well as the co-existence mechanisms between 5G NR V2X and LTE V2X. We also review the use cases, the system architecture, and describe the evaluation methodology and simulation assumptions for 5G NR V2X. Finally, we provide an outlook on possible 5G NR V2X enhancements, including those identified within Release 17.
Safety is one of the key requirements for automated vehicles and fault diagnosis is an effective technique to enhance the vehicle safety. The model-based fault diagnosis method models the fault into ...the system model and estimates the faults by observer. In this article, to avoid the complexity of designing observer, we investigate the problem of steering actuator fault diagnosis for automated vehicles based on the approach of model-based support vector machine (SVM) classification. The system model is utilized to generate the residual signal as the training data and the data-based algorithm of the SVM classification is employed to diagnose the fault. Due to the phenomena of data unbalance induced poor performance of the data-driven method, an undersampling procedure with the approach of linear discriminant analysis and a threshold adjustment using the algorithm of grey wolf optimizer are proposed to modify and improve the performance of classification and fault diagnosis. Various comparisons are carried out based on widely used datasets. The comparison results show that the proposed algorithm has superiority on the classification over existing methods. Experimental results and comparisons of an automated vehicle illustrate the effectiveness of the proposed algorithm on the steering actuator fault diagnosis.
•Anxiety is a potential predictor of user role adaptation toward highly automated driving.•Trust and situational awareness have a mediating effect.•The automated vehicle driving styles moderate ...driver adaptation performance.•The findings are helpful for personalized automated system design.
The emergence of highly automated driving technology provides safe and convenient travel while also causing user inadaptation. Therefore, based on human factors engineering, it is necessary to study highly automated vehicles (HAVs) that meet different user needs. Thus, this study aims to investigate the relationships between state anxiety, situational awareness, trust, and role adaptation. The adaptation model is constructed to conduct a study on the adaptation of HAVs with different automated styles when user roles change from driver to passenger. Simulated riding was conducted in the HAV experiment (N = 117), collecting scale data after each participant had experienced each automated driving style. A structural equation modeling approach was applied to analyze the adaptation model based on scale data. The results showed that there was a significant correlation between state anxiety, situational awareness, trust, and role adaptation. State anxiety has a significant negative predictive effect on trust, situational awareness, and role adaptation. In addition to its direct impact on role adaptation, state anxiety also has an indirect effect on role adaptation through situational awareness and trust. Furthermore, the automated driving style has been confirmed to have a moderating role in the relationship between the direct and indirect effects of state anxiety and role adaptation. Our findings contribute to multiple streams of the literature and have important implications for designing personalized automated driving to improve user acceptance.
This paper presents a novel cooperative-driving prediction and planning framework for dynamic environments based on the methods of game theory. The proposed algorithm can be used for highly automated ...driving on highways or as a sophisticated prediction module for advanced driver-assistance systems with no need for intervehicle communication. The main contribution of this paper is a model-based interaction-aware motion prediction of all vehicles in a scene. In contrast to other state-of-the-art approaches, the system also models the replanning capabilities of all drivers. With that, the driving strategy is able to capture complex interactions between vehicles, thus planning maneuver sequences over longer time horizons. It also enables an accurate prediction of traffic for the next immediate time step. The prediction model is supported by an interpretation of what other drivers intend to do, how they interact with traffic, and the ongoing observation. As part of the prediction loop, the proposed planning strategy incorporates the expected reactions of all traffic participants, offering cooperative and robust driving decisions. By means of experimental results under simulated highway scenarios, the validity of the proposed concept and its real-time capability is demonstrated.
The benefits of autonomous vehicles (AVs) are widely acknowledged, but there are concerns about the extent of these benefits and AV risks and unintended consequences. In this article, we first ...examine AVs and different categories of the technological risks associated with them. We then explore strategies that can be adopted to address these risks, and explore emerging responses by governments for addressing AV risks. Our analyses reveal that, thus far, governments have in most instances avoided stringent measures in order to promote AV developments and the majority of responses are non-binding and focus on creating councils or working groups to better explore AV implications. The US has been active in introducing legislations to address issues related to privacy and cybersecurity. The UK and Germany, in particular, have enacted laws to address liability issues; other countries mostly acknowledge these issues, but have yet to implement specific strategies. To address privacy and cybersecurity risks strategies ranging from introduction or amendment of non-AV specific legislation to creating working groups have been adopted. Much less attention has been paid to issues such as environmental and employment risks, although a few governments have begun programmes to retrain workers who might be negatively affected.
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