Technological and infrastructural prerequisites for the deployment of the first shared Autonomous Vehicle Pilot Test Project in Malta

Automated vehicles are leading to a significant revolution in road transportation systems, with extensive challenges to mobility through several factors, including economic, legal, social, psychological, and technological aspects. Various countries around Europe have tested and piloted autonomous vehicles on their roads and subsequently published research on their approach towards reaching such goals and also on findings from their various projects. However smaller peripheral countries tend to fall behind, whenever innovative technologies are being researched and tested. Research on Malta’s level of preparedness for the introduction of autonomous vehicles is limited. This research project deals with an analysis of the expected impacts of shared autonomous vehicles on the physical and digital road infrastructures and assesses the current infrastructure preparedness on a local scale. The project aims to provide Malta with a new mobility solution, that is sustainable and technologically advanced, that will at the same time increase the users’ choices of alternative transport. Through various meetings and consultations with local stakeholders, it emerges that Malta is still in the very initial planning stages for the introduction of Autonomous Vehicles and that an immediate intervention on Malta’s current physical and digital infrastructure is required to enable the actual implementation of shared autonomous mobility field testing. The outcomes of this research project include a roadmap and recommendations to local authorities, which shall pave the way for the first shared autonomous shuttle pilot project to take place on Maltese roads.This research work forms part of Project MISAM financed by the Malta Council for Science & Technology, for and on behalf of the Foundation for Science and Technology, through the FUSION: R&I Research Excellence Programme’. The authors would also like to thank Infrastructure Malta and Debono Group, the collaborators on this project.peer-reviewe

Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving

In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving-related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers’ takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers’ takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not

Stakeholders’ Trust in Automated Driving

As automated driving continues to evolve, trust plays a pivotal role in its adoption and acceptance in society. This thesis aims to explore different stakeholders’ trust in automated driving systems by examining factors that influence said trust. A questionnaire was developed in order to gather recent data on trust in automated driving systems and it was used to analyze factors that influence trust in automated driving. The questionnaire was also used to identify possible methods that could improve trust. The questionnaire in this thesis also identifies which types of stakeholders would trust automated driving, for example by examining familiarity. Furthermore, the study addresses the issue of vulnerable road users reasons for distrust in automated driving. Identifying these issues in such fields as ethics, legislation, or safety can lead to enhancement of development strategies, which in turn would enhance stakeholders’ trust in automated driving. This thesis provides valuable insights into the relationship between trust, technology adoption, and user apprehensions on automated driving, with a goal of improving the development considerations in the rapidly evolving transportation technology

Improving Automated Driving through Planning with Human Internal States

This work examines the hypothesis that partially observable Markov decision process (POMDP) planning with human driver internal states can significantly improve both safety and efficiency in autonomous freeway driving. We evaluate this hypothesis in a simulated scenario where an autonomous car must safely perform three lane changes in rapid succession. Approximate POMDP solutions are obtained through the partially observable Monte Carlo planning with observation widening (POMCPOW) algorithm. This approach outperforms over-confident and conservative MDP baselines and matches or outperforms QMDP. Relative to the MDP baselines, POMCPOW typically cuts the rate of unsafe situations in half or increases the success rate by 50%.Comment: Preprint before submission to IEEE Transactions on Intelligent Transportation Systems. arXiv admin note: text overlap with arXiv:1702.0085

Detecting Driver Sleepiness Using Consumer Wearable Devices in Manual and Partial Automated Real-Road Driving

Driver sleepiness constitutes a well-known traffic safety risk. With the introduction of automated driving systems, the chance of getting sleepy and even falling asleep at wheel could increase further. Conventional sleepiness detection methods based on driving performance and behavior may not be available under automated driving. Methods based on physiological measurements such as heart rate variability (HRV) becomes a potential solution under automated driving. However, with reduced task load, HRV could potentially be affected by automated driving. Therefore, it is essential to investigate the influence of automated driving on the relation between HRV and sleepiness. Data from real-road driving experiments with 43 participants were used in this study. Each driver finished four trials with manual and partial automated driving under normal and sleep-deprived condition. Heart rate was monitored by consumer wearable chest bands. Subjective sleepiness based on Karolinska sleepiness scale was reported at five min interval as ground truth. Reduced heart rate and increased overall variability were found in association with severe sleepy episodes. A binary classifier based on the AdaBoost method was developed to classify alert and sleepy episodes. The results indicate that partial automated driving has small impact on the relationship between HRV and sleepiness. The classifier using HRV features reached area under curve (AUC) = 0.76 and it was improved to AUC = 0.88 when adding driving time and day/night information. The results show that commercial wearable heart rate monitor has the potential to become a useful tool to assess driver sleepiness under manual and partial automated driving