Search results

Purpose – Driver education and licensing are two mechanisms used to reduce crash rates. The purpose of this chapter is to provide an overview of these countermeasures and consider how simulators can be used to augment more traditional approaches.

Approach – A literature review was undertaken evaluating key concepts in driver licensing including graduated driver licensing (GDL), the role of parents in licensing, compliance and enforcement, driver testing and how the driver licensing system impacts on levels of unlicensed driving. Literature regarding driver education for individuals who have and not yet obtained a licence was also reviewed.

Findings – GDL is a successful countermeasure for reducing the crash rates of young novice drivers as it limits their exposure to higher risk situations. The support for driver education initiatives is mixed. As there are big differences between education programs, there is a need to consider each program on its own merits. Driving simulators provide a safe environment for novices to gain experience. In particular, they may be bifacial for development of hazard perception and visual scanning skills.

Practical Implications – GDL systems should be introduced where appropriate. Existing systems should be strengthened where possible by including additional, best-practice and restrictions. When considering driver education as a countermeasure, the type of program is very important. Education programs that have been shown to increase crashes should not be introduced. Further research and development are necessary to ensure that driver education and licensing adequately equip novice drivers with the skills necessary to drive in the 21st century.

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 many cognitive training tests purporting to both measure older people’s cognitive performance, several of which come with associated training that are deemed to improve cognition. This chapter describes cognitive tests that have been claimed to be linked to driver behaviour, and that training on them could improve driver behaviour. Of special interest are tests that could be completed at home on a computer, as it is suggested this could capture many individuals who are worried about attending a driver assessment centre and are not likely to be referred. Findings suggest that UFOV (Useful Field of View) Time Making Trail (A and B) and Dual N have research suggesting that training on them could improve driver performance for older drivers. However, the robustness of the research is debateable. There are also two physiological tests – a neck and shoulder and a general fitness test that also show promising results for improving driver performance. In addition, education and training is purported to improve driver behaviour, but although there is positive feedback from older people who attend and some short-term improvements, research on long-term improvements on driver behaviour are not yet evident. Overall, there are promising results from individual cognitive, physiological tests and from education and training suggesting that reflection on action and feedback from the task is important to improving driver performance but more research is needed.

This paper aims to present a picture of the training and assessment of drivers in Algeria, to identify the key weaknesses that characterize it, and to propose improvements.

The methodology adopted was to question driving instructors, driving examiners and drivers themselves on the operation of driving schools, training programmes and the teaching methods used. The research was conducted from January 2008 to December 2009. The sample consists of 960 individuals, distributed as follows: 240 driving schools, 672 drivers and 48 examiners. The work then involved direct observation of training and assessment to ascertain the actual conditions under which these operate.

Results shows the adverse conditions in which training takes place on one hand and field practices in contrast with the principles of safety and quality on the other. Quality training requires an improvement of those conditions and practices.

This study is a first qualitative approach to specific driver training in Algeria. The results should produce a statement on the situation and provide recommendations to improve training and evaluation.

This case explores how driver training school create experience value for their trainees. It describes the development of driver training industry, the foundation and new training mode of Rongan Driving School, changes and challenges of environment for Rongan facing and so on, which will guide readers to discuss six influence factors of customer experience, six dimensions of customer-experience value, the relationship between them, and the influence of social environment. Rongan's innovative training mode of “pay after learning, time-based billing, one car for one person”, provides a good training experience for driving trainees. It has become the benchmark of the national driving training industry within six years.

Increasing demand on rail transport speeds up the introduction of new technical systems to optimize the rail traffic and increase competitiveness. Remote control of trains is seen as a potential layer of resilience in railway operations. It allows for operating and controlling automated trains and communicating and coordinating with other stakeholders of the railway system. This paper aims to present the first results of a multi-phased simulator study on the development and optimization of remote train driving concepts from the operators’ point of view.

The presented concept was developed by benchmarking good practices. Two phases of iterative user tests were conducted to evaluate the user experience and preferences of the developed human-machine-interface concept. Basic training requirements were identified and evaluated.

Results indicate positive feedback on the overall system as a fallback solution. HMI elicited positive emotions regarding pleasure and dominance, but low arousal levels. Train drivers had more conservative views on the system compared to signalers and students. The training activities achieved increased awareness and understanding of the system for future operators. Inclusion of potential users in the development of future systems has the potential to improve user acceptance. The iterative user experiments were useful in obtaining some of the needs and preferences of different user groups.

Multi-phase user tests were conducted to identify and to evaluate the requirements and preferences of remote operators using a simplified HMI. Training analysis provides important aspects to consider for the training of future users.