A complete set of scattering observables would be required in order to study the details of the neutron-nucleus interaction. In particular, we are interested in what we can learn from the RIA in order to build up a phenomenological optical potential. We have used the relativistic impulse approximation (RIA) to study the newly measured neutron total cross section data for 208Pb, 90Zr, and 40Ca. Through this research line, we will advance the knowledge of AV-HRU cooperation and contribute to the safety and efficiency of mixed traffic.
Finally, concepts of AVs that provide explicit gestures will be developed, tested, and demonstrated to industry on public roads. Third, a closed-loop model that describes HRU-HRU/AV interactions will be developed and used to predict which explicit gestures HRUs need to receive from AVs. Second, we will create two unique facilities (linked simulators and a test track setup with synchronized data logging) to determine the effectiveness of implicit anthropomorphic and implicit mechanistic gestures in staged HRU-HRU/AV encounters. The effectiveness of the gestures will be assessed regarding whether HRUs are willing and able to use the AVs’ gestures, and in terms of safety and efficiency of the AV-HRU encounters.įirst, we will perform naturalistic on-road studies and large online surveys to identify how HRUs apply implicit and explicit gestures, and to derive prototypical traffic scenarios where gesturing is important. We will study the largely unexplored topic of how HRUs communicate with each other, and examine whether AVs’ gestures should be human-like (anthropomorphic) or nonhuman (mechanistic), and implicit (embedded in vehicle motion) or explicit (visual/auditory signs). This research aims to devise novel AV-to-HRU communication methods. Because AVs have excellent sensor and computational abilities, the opportunity arises to develop AV-to-HRU communication that not only emulates but also surpasses the information content of HRU-to-HRU communication. In the coming decades, our roads will be populated with an unprecedented mixture of automated vehicles (AVs) and human road users (HRUs) such as pedestrians, cyclists, and manually driven cars.Ī bottleneck is that current AVs do not communicate to HRUs, making AV-HRU encounters inefficient and potentially accident-prone as compared to HRUs who fluently move in traffic through mutual communication of their states and intentions. We combine engineering domains such as simulator hardware, traffic flow theory, control theory, and mathematical driver modelling with psychological domains such as human action and perception, cognitive modelling, vigilance, distraction, psychophysiology, and mode/situation awareness, to optimally address the interdisciplinary domain of human factors. HFauto bridges the gap between engineers and psychologists through a multidisciplinary research and training programme. What are the effects of HAD on accident risk and transport efficiency? How can the automation understand the driver’s state and intentions? How should human-machine-interfaces (HMI) be designed to support transitions between automated and manual control? However, before automated driving can be safely deployed on public roads we have to deal with imminent human factors questions, such as: Highly automated driving (HAD) has the potential to resolve these problems and major car makers foresee that HAD will be technically ready for commercialisation within one decade from now.
Road transport is an essential part of society but the burden of traffic crashes, congestion, and poll ution is enormous. To generate knowledge on Human Factors of automated driving towards safer road transportation.
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