Safe, Ethical and Efficient Autonomous Vehicle Navigation Algorithms

英国自动驾驶交通行业是一个快速增长的行业，专注于自动驾驶汽车的开发、测试和部署。公众对自动驾驶汽车的信任是一个关键因素，可以显著影响自动驾驶技术的采用和广泛接受。安全方面存在担忧，报告的事故可能会削弱公众对自动驾驶技术的信任。还有一些问题是缺乏对技术的理解，以及将控制权交给机器的意愿。对自主技术缺乏了解可能会导致完全不信任，并可能导致对该技术的忽视。英国汽车工业也占每年二氧化碳排放量的很大一部分。监管机构要求汽车行业脱碳的压力也越来越大。这些挑战的一个可能的解决方案来自于导航算法的发展，这些算法考虑了安全、社会行为和道德（道德上可接受的算法）。使用基于模型的方法（如模型预测控制）和智能导航算法设计，将开发一种方法来提高安全性，操作道德上可接受的算法，并提高自动驾驶车辆的效率。在智能导航算法方法的开发中（安全、社会行为和道德因素），将考虑前馈技术（例如，外部道路摄像机、无人机摄像机和标牌）。将进一步考虑技术的过渡，例如，从人类驾驶的车辆到自动驾驶的车辆，以及各种动力系统（例如，燃烧、混合动力、电动和氢）。我们的愿景是开发算法，通过提高安全性、减少排放、透明的可接受/道德方法来建立公众对自动驾驶汽车的信任。开发的导航算法将使用伯明翰主要道路的数据进行测试（即，交叉口、环形交叉口和高速公路），并传播预计的好处，以提高公众对该技术的信任。

The UK autonomous transport industry is a rapidly growing sector focused on the development, testing, and deployment of self-driving vehicles. Public trust in self-driving vehicles is a critical factor that can significantly impact the adoption and widespread acceptance of autonomous technology. There are concerns with safety, where reported accidents can erode public trust in autonomous technology. There are also issues with the lack of understanding of the technology and the willingness to hand over control to a machine. The lack of understanding of autonomous technology can lead to complete mistrust, and potentially the dismissal of the technology. The UK automotive industry also contributes to a large share of the annual CO2 emissions. There is also increasing pressure from regulatory bodies for decarbonisation of the automotive sector. A possible solution to these challenges comes from the development of navigation algorithms that factor in safety, social actions, and ethics (morally accepted algorithms). Using a model-based approach (such as model predictive control) with intelligent navigation algorithm design, an approach will be developed to improve the safety, operate morally acceptable algorithms, and improve the efficiency of autonomous vehicles. In the development of intelligent navigation algorithm approaches (that factor in safety, social actions, and ethics), feedforward technology will be considered (e.g., external road cameras, drone cameras and signage). Further considerations will be given to the transition of technologies, e.g., from human-driven vehicles to autonomous vehicles, and the various powertrains (e.g., combustion, hybrid, electric and hydrogen). It is our vision to develop algorithms that build public trust in self-driving vehicles through improved safety, reduced emissions, accepted/ethical approaches that are transparent. The developed navigation algorithms will be tested using data from key Birmingham roads (i.e., junctions, roundabouts, and highways), with the projected benefits disseminated to improve public trust in the technology.