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The successful and commercial use of self-driving/driverless/unmanned/automated car will make human life easier. The paper aims to discuss this issue.

This paper reviews the key technology of a self-driving car. In this paper, the four key technologies in self-driving car, namely, car navigation system, path planning, environment perception and car control, are addressed and surveyed. The main research institutions and groups in different countries are summarized. Finally, the debates of self-driving car are discussed and the development trend of self-driving car is predicted.

This paper analyzes the key technology of self-driving car and illuminates the state-of-art of the self-driving car.

The main research contents and key technology have been introduced. The research progress as well as the research institution has been summarized.

Autonomous technologies represent an increasingly important, but at the same time controversial technological field with enormous potential. From a consumer perspective, however, the growing autonomy of technologies might result in a perceived loss of control, which can lead to consumer resistance. Given the practical and theoretical relevance, this research examines antecedents to consumer adoption of autonomous technologies in the context of self-driving cars.

This article looks through the lens of the technology acceptance model and conducts structural equation modeling.

The study validates the positive effect of perceived usefulness on behavioral intention to adopt self-driving cars. The results further suggest that individuals with a generally negative attitude toward technologies are afraid that they might not be capable of handling the new technology. Moreover, further mediation analyses reveal that perceived ease of use and perceived usefulness help us to explain the indirect effects of novelty seeking and technology anxiety on adoption intention.

The results imply that users' perceptions of an autonomous technology's usefulness are an important determinant of technology adoption. Adoption barriers could be overcome by emphasizing the usability of the new technology. On the other hand, individuals who enjoy using the old technology may be persuaded by arguments that focus on the usefulness of the new technology rather than its ease of use.

Self-driving automobiles will change our perception of mobility. It is important to understand the mechanisms that drive the adoption of such innovations.

This paper aims to develop a robust navigation enhancement framework to handle one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents.

In this paper, the main idea is to fully exploit the consistent features among spatio-temporal data and thus detect the anomalies and build residual channels to reconstruct the abnormal information. The authors first develop an anomaly detection algorithm, then followed by a corresponding disturbed information reconstruction network which has strong robustness to address both the nature disturbances and external attacks. Finally, the authors introduce a fully end-to-end resilient navigation performance enhancement framework to improve the driving performance of existing self-driving models under attacks and disturbances.

Comparison results on CARLA platform and real experiments demonstrate strong resilience of the authors’ approach which enhances the navigation performance under disturbances and attacks.

Reliable and resilient navigation performance under various nature disturbances and even external attacks is one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents. The information reconstruction approach provides a resilient navigation performance enhancement method for existing self-driving models.

Recent studies on commuter parking in an age of fully autonomous vehicles (FAVs) suggest, that the number of parking spaces close to the workplace demanded by commuters will decline because of the capability of FAVs to return home, to seek out (free) parking elsewhere or just cruise. This would be good news because, as of today, parking is one of the largest consumers of urban land and is associated with substantial costs to society. None of the studies, however, is concerned with the special case of employer-provided parking, although workplace parking is a widespread phenomenon and, in many instances, the dominant form of commuter parking. The purpose of this paper is to analyze whether commuter parking will decline with the advent of self-driving cars when parking is provided by the employer.

This study looks at commuter parking from the perspective of both the employer and the employee because in the case of employer-provided parking, the firm’s decision to offer a parking space and the incentive of employees to accept that offer are closely interrelated because of the fringe benefit character of workplace parking. This study develops an economic equilibrium model that explicitly maps the employer–employee relationship, considering the treatment of parking provision and parking policy in the income tax code and accounting for adverse effects from commuting, parking and public transit. This study determines the market level of employer-provided parking in the absence and presence of FAVs and identifies the factors that drive the difference. This study then approximates the magnitude of each factor, relying on recent (first) empirical evidence on the impacts of FAVs.

This paper’s analysis suggests that as long as distortive (tax) policy favors employer-provided parking, FAVs are no guarantee to end up with less commuter parking.

This study’s findings imply that in a world of self-driving cars, policy intervention related to work commuting (e.g. fringe benefit taxation or transport pricing) might be even more warranted than today.

This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.

The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.

This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.

Recently, autonomous vehicles (AV) acceptance has been studied intensively. This paper aims to map and analyze the bibliometric characteristics of AV acceptance literature. Furthermore, this research aims to identify research gaps and propose future research opportunities.

The bibliometric analysis was performed. Scopus database was used as the source of the literature. This study selected and analyzed 297 AV acceptance papers. The performance and science mapping analysis were performed.

The developed countries tended to dominate the topic. The publication outlet tended to be in transportation or technology journals. There were four research themes in existing literature. Technology acceptance model (TAM) and UTAUT2 tended to be used for explaining AV acceptance. AV acceptance studies tended to use two types of psychological concepts for understanding AV acceptance, namely risk related concepts and functional utilitarian benefit related concepts. In the context of research design, quantitative approach tended to be used. Self-driving feature was the most exploited feature of AV in the existing literature. Three research gaps were mapped and future research opportunities were proposed.

This paper provided a comprehensive information that allowed scientists to develop future research on AV acceptance.

There is lack of paper that discussed the bibliometric characteristics of AV acceptance literature. This paper fulfilled the gap.

Many countries and regions have recognised the potential of automated transport as a solution to cover mobility needs in a sustainable way. They have implemented dedicated strategies and allowed trial operations of Automated Vehicles (AVs) within their national frameworks.

This chapter conducts an analysis of the legal frameworks for AV trial operations in 11 European countries. It reviews existing laws and regulations and includes results from an online survey with national stakeholders and experts experienced in AV testing.

The results reveal very different approaches among European countries. Moreover, results indicate a stronger focus on technical safety aspects of the vehicles rather than on operational procedures and mobility integration, such as incorporating AV services into existing public transport systems.

This high level of disparity between the different European legal frameworks poses a considerable barrier to a rollout of the technologies and methodologies for AVs without cross-border and cross-supplier conflicts. Furthermore, when moving to the deployment of real services in the near future a common European framework and a stronger focus on operational procedures are essential for the implementation of automated transport services in order to cover the mobility needs of people in a more sustainable way (e.g. first/last mile to public transport).

European countries should further integrate operational aspects in the terms of services that are integrated in public transport, align deployment of AVs with national and local sustainability goals and focus on use cases beyond private vehicles to foster the transition to a more sustainable future of transport.