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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.

There are ethical, legal, social and economic arguments surrounding the subject of autonomous vehicles. This paper aims to discuss some of the arguments to communicate one of the current issues in the rising field of artificial intelligence.

Making use of widely available literature that the author has read and summarised showcasing her viewpoints, the author shows that technology is progressing every day. Artificial intelligence and machine learning are at the forefront of technological advancement today. The manufacture and innovation of new machines have revolutionised our lives and resulted in a world where we are becoming increasingly dependent on artificial intelligence.

Technology might appear to be getting out of hand, but it can be effectively used to transform lives and convenience.

From robotics to autonomous vehicles, countless technologies have and will continue to make the lives of individuals much easier. But, with these advancements also comes something called “future shock”.

Future shock is the state of being unable to keep up with rapid social or technological change. As a result, the topic of artificial intelligence, and thus autonomous cars, is highly debated.

The study will be of interest to researchers, academics and the public in general. It will encourage further thinking.

This is an original piece of writing informed by reading several current pieces. The study has not been submitted elsewhere.

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 develop a holistic approach to the assessment of dynamic capabilities (DCs). Holistic refers to incorporating all DCs of an organisation relevant for determining and executing the firm's strategy.

A two-phase study was conducted. First, secondary sources, such as media, industry and annual reports, are being used to initially assess CASE (connected, autonomous, shared and electric) and implications for incumbent car manufacturers in a structured way. Second, semi-structured interviews with automotive managers and further automotive stakeholders offer in-depth insights into CASE, as well as incumbents' strategies and the underlying rationale.

The proposed framework for assessing DCs offers a holistic approach and provides new angles of analysis. First, the time dimension is considered using scenarios since timing is vital in strategy and implementation. Second, capabilities are broken down into technological and non-technological, sharpening strategic decision-making of automakers. Third, the analysis considers external VUCA (volatility, uncertainty, complexity and ambiguity) as they interplay with internal DCs.

Further testing of the proposed DC assessment approach offers a promising opportunity for future research. This paper focuses on the automotive industry, but it is worth investigating the extent to which the approach can be used in other dynamic industries, such as finance or retail.

The approach proposed highlights the importance and nuances of considering external perspectives in the DC assessment and the relevance of non-technological capabilities in the automotive industry. Thereby, it contributes to the literature on capability assessments and the operationalisability of the DC lens.