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The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

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.

The purpose of this paper is to explore consumers’ understanding of autonomous driving by comparing perceptions of occasional drivers (ODs) and frequent drivers (FDs).

Data were gathered through semi-structured interviews with 41 drivers. Their responses were categorized into thematic categories or topics on the basis of content analysis, and the topics were structured based on the core-periphery model. Finally, the authors visualized the structure on a perceptual map by adopting a maximum tree approach.

Respondents’ understanding of autonomous driving were categorized into 10 topics. There were significant differences in topics and their relationships between ODs and FDs. Findings also show that FD can better detect hazardousness from autonomous driving environments than ODs.

Differently from prior studies’ focus on its technological aspect and some derived benefits, the study examines it from the viewpoint of consumers, who are critical participants in the dissemination of autonomous driving.

The findings suggest that rather than focusing on developing the highest level of autonomous cars, developing in an evolutionary way by adding automated functions to existing cars can be the better strategy to dominate the autonomous vehicle market.

This study is a pioneering work in that it can be an initial empirical work on autonomous driving from the customer standpoint.

Collaborative robotics and autonomous driving are fairly new disciplines, still with a long way to go to achieve goals, set by the research community, manufacturers and users. For technologies like collaborative robotics and autonomous driving, which focus on closing the gap between humans and machines, the physical, psychological and emotional needs of human individuals becoming increasingly important in order to ensure effective and safe human–machine interaction. The authors' goal was to conceptualize ways to combine experience from both fields and transfer artificial intelligence knowledge from one to another. By identifying transferable meta-knowledge, the authors will increase quality of artificial intelligence applications and raise safety and contextual awareness for users and environment in both fields.

First, the authors presented autonomous driving and collaborative robotics and autonomous driving and collaborative robotics' connection to artificial intelligence. The authors continued with advantages and challenges of both fields and identified potential topics for transferrable practices. Topics were divided into three time slots according to expected research timeline.

The identified research opportunities seem manageable in the presented timeline. The authors' expectation was that autonomous driving and collaborative robotics will start moving closer in the following years and even merging in some areas like driverless and humanless transport and logistics.

The authors' findings confirm the latest trends in autonomous driving and collaborative robotics and expand them into new research and collaboration opportunities for the next few years. The authors' research proposal focuses on those that should have the most positive impact to safety, complement, optimize and evolve human capabilities and increase productivity in line with social expectations. Transferring meta-knowledge between fields will increase progress and, in some cases, cut some shortcuts in achieving the aforementioned goals.

Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase.

This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features.

The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction.

In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.

The purpose of this paper is to develop a model for autonomous cars to establish trusted parties by combining distributed ledgers and self-driving cars in the traffic to provide single version of the truth and thus build public trust.

The model, which the authors call Witness of Things, is based on keeping decision logs of autonomous vehicles in distributed ledgers through the use of vehicular networks and vehicle-to-vehicle/vehicle-to-infrastructure (or vice versa) communications. The model provides a single version of the truth and thus helps enable the autonomous vehicle industry, related organizations and governmental institutions to discover the true causes of road accidents and their consequences in investigations.

In this paper, the authors explored one of the potential effects of blockchain protocol on autonomous vehicles. The framework provides a solution for operating autonomous cars in an untrusted environment without needing a central authority. The model can also be generalized and applied to other intelligent unmanned systems.

This study proposes a blockchain protocol-based record-keeping model for autonomous cars to establish trusted parties in the traffic and protect single version of the truth.