A Lie on Sharing Economy: Solutions for Uber Drivers’ Dilemma When Self-Driving Cars Arrive

Self-driving cars have been started to be quickly manufactured and tested for real driving on the road by various companies such as Tesla, Mercedes-Benz, General Motors, Google, and Uber. Uber’s involvement in developing self-driving technology is not a secret – its autonomous vehicle pilot program has been widely mentioned and discussed. However, it seems that people forget one thing when they read the news about the launch of driverless taxi by Uber: Uber is also a leading company in sharing economy that provides a technology platform to collaborate with its ‘registered partners’—who are human drivers and will be replaced by self-driving technology soon. What is the solution for Uber drivers’ dilemma – stay or prepare to leave ridesharing business? This research proposes to provide a research model to answer this question with the ‘real’ factors that will make customers choose ridesharing service with human drivers. A two-step approach with detailed survey on customers as well as semi-structured interview on drivers will be conducted. It is expected that this research will help current Uber drivers know their positions in the dynamic market. People who are facing the similar dilemma (i.e. will soon be substituted by future technologies) will also learn how to survive in the competitive business environment

An active inference model of car following: Advantages and applications

Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability, i.e., an understandable link between input data and output behaviors. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data-driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision making process and correct model errors attributable to limited training data. The results indicate that the AIDA is a promising alternative to black-box data-driven models and suggest a need for further research focused on modeling driving style and model training with more diverse datasets

On the Road with an Autonomous Passenger Shuttle: Integration in Public Spaces

The integration of autonomous vehicles (AVs) onto public roads presents both technical and social challenges. Public understanding and acceptance of AVs requires engagement with people who live in, work at or visit cities where they are deployed on public road networks. We investigate the impact of one of the first placements of AV passenger transport on public roadways: the Sion >. This late- breaking research presents preliminary results from interviews with local shopkeepers, residents, pedestrians and drivers to understand their attitudes and opinions of the shuttle. We also discuss video- based fieldwork that demonstrates how drivers negotiate next moves with one another through their windscreens using embodied signals such as gestures, lip-reading, and head nods to coordinate and manage a traffic situation. Finally, we consider the implications for how fully autonomous vehicles might be designed to take into account the subtle negotiations that road users engage in to coordinate with one another

What Do You Want From Me? Adapting Systems to the Uncertainty of Human Preferences

Autonomous systems, like drones and self-driving cars, are becoming part of our daily lives. Multiple people interact with them, each with their own expectations regarding system behaviour. To adapt system behaviour to human preferences, we propose and explore a game-theoretic approach. In our architecture, autonomous systems use sensor data to build game-theoretic models of their interaction with humans. In these models, we represent human preferences with types and a probability distribution over them. Game-theoretic analysis then outputs a strategy, that determines how the system should act to maximise utility, given its beliefs over human types. We showcase our approach in a search-and-rescue (SAR) scenario, with a robot in charge of locating victims. According to social psychology, depending on their identity some people are keen to help others, while some prioritise their personal safety. These social identities define what a person favours, so we can map them directly to game-theoretic types. We show that our approach enables a SAR robot to take advantage of human collaboration, outperforming non-adaptive configurations in average number of successful evacuations

Planning Perspectives on Rural Connected, Autonomous and Electric Vehicle Implementation

Connected, autonomous and electric vehicles (CAEV) are a powerful combined transport technology looking to disrupt the automotive sector and drive the transition to safe, accessible, clean and sustainable transport systems. The trialling of private, public and shared CAEV technologies is occurring in cities around the world; however, historically isolated and transport-poor rural communities may have the most to gain from CAEV implementation. Despite the accessibility and transport safety needs of rural communities, rural CAEV trials are few in the UK. Therefore, this paper investigates the hypothesis that the lack of rural implementation research and trials means that rural transport planners are ill-informed and uncertain of both the potential of CAEVs and their implementation requirements to meet rural community transport needs. This investigation consists of consultations with UK-based transport planning professionals to establish their perspectives on CAEV technologies and their rural implementation potential. The findings show that 96% of transport planners lack sufficient understanding of CAEV technology and its implementation challenges. However, the findings also highlight a willingness, given the opportunity, for transport planners to engage with CAEV technologies and apply them to specifically rural scenarios

Real-time simulator of collaborative and autonomous vehicles

Durant ces dernières décennies, l’apparition des systèmes d’aide à la conduite a essentiellement été favorisée par le développement des différentes technologies ainsi que par celui des outils mathématiques associés. Cela a profondément affecté les systèmes de transport et a donné naissance au domaine des systèmes de transport intelligents (STI). Nous assistons de nos jours au développement du marché des véhicules intelligents dotés de systèmes d’aide à la conduite et de moyens de communication inter-véhiculaire. Les véhicules et les infrastructures intelligents changeront le mode de conduite sur les routes. Ils pourront résoudre une grande partie des problèmes engendrés par le trafic routier comme les accidents, les embouteillages, la pollution, etc. Cependant, le bon fonctionnement et la fiabilité des nouvelles générations des systèmes de transport nécessitent une parfaite maitrise des différents processus de leur conception, en particulier en ce qui concerne les systèmes embarqués. Il est clair que l’identification et la correction des défauts des systèmes embarqués sont deux tâches primordiales à la fois pour la sauvegarde de la vie humaine, à la fois pour la préservation de l’intégrité des véhicules et des infrastructures urbaines. Pour ce faire, la simulation numérique en temps réel est la démarche la plus adéquate pour tester et valider les systèmes de conduite et les véhicules intelligents. Elle présente de nombreux avantages qui la rendent incontournable pour la conception des systèmes embarqués. Par conséquent, dans ce projet, nous présentons une nouvelle plateforme de simulation temps-réel des véhicules intelligents et autonomes en conduite collaborative. Le projet se base sur deux principaux composants. Le premier étant les produits d’OPAL-RT Technologies notamment le logiciel RT-LAB « en : Real Time LABoratory », l’application Orchestra et les machines de simulation dédiées à la simulation en temps réel et aux calculs parallèles, le second composant est Pro-SiVIC pour la simulation de la dynamique des véhicules, du comportement des capteurs embarqués et de l’infrastructure. Cette nouvelle plateforme (Pro-SiVIC/RT-LAB) permettra notamment de tester les systèmes embarqués (capteurs, actionneurs, algorithmes), ainsi que les moyens de communication inter-véhiculaire. Elle permettra aussi d’identifier et de corriger les problèmes et les erreurs logicielles, et enfin de valider les systèmes embarqués avant même le prototypage

A framework for the synergistic integration of fully autonomous ground vehicles with smart city

Most of the vehicle manufacturers aim to deploy level-5 fully autonomous ground vehicles (FAGVs) on city roads in 2021 by leveraging extensive existing knowledge about sensors, actuators, telematics and Artificial Intelligence (AI) gained from the level-3 and level-4 autonomy. FAGVs by executing non-trivial sequences of events with decimetre-level accuracy live in Smart City (SC) and their integration with all the SC components and domains using real-time data analytics is urgent to establish better swarm intelligent systems and a safer and optimised harmonious smart environment enabling cooperative FAGVs-SC automation systems. The challenges of urbanisation, if unmet urgently, would entail severe economic and environmental impacts. The integration of FAGVs with SC helps improve the sustainability of a city and the functional and efficient deployment of hand over wheels on robotized city roads with behaviour coordination. SC can enable the exploitation of the full potential of FAGVs with embedded centralised systems within SC with highly distributed systems in a concept of Automation of Everything (AoE). This paper proposes a synergistic integrated FAGV-SC holistic framework - FAGVinSCF in which all the components of SC and FAGVs involving recent and impending technological advancements are moulded to make the transformation from today's driving society to future's next-generation driverless society smoother and truly make self-driving technology a harmonious part of our cities with sustainable urban development. Based on FAGVinSCF, a simulation platform is built both to model the varying penetration levels of FAGV into mixed traffic and to perform the optimal self-driving behaviours of FAGV swarms. The results show that FAGVinSCF improves the urban traffic flow significantly without huge changes to the traffic infrastructure. With this framework, the concept of Cooperative Intelligent Transportation Systems (C-ITS) is transformed into the concept of Automated ITS (A-ITS). Cities currently designed for cars can turn into cities developed for citizens using FAGVinSCF enabling more sustainable cities

A Framework for the Synergistic Integration of Fully Autonomous Ground Vehicles With Smart City

Most of the vehicle manufacturers aim to deploy level-5 fully autonomous ground vehicles (FAGVs) on city roads in 2021 by leveraging extensive existing knowledge about sensors, actuators, telematics and Artificial Intelligence (AI) gained from the level-3 and level-4 autonomy. FAGVs by executing non-trivial sequences of events with decimetre-level accuracy live in Smart City (SC) and their integration with all the SC components and domains using real-time data analytics is urgent to establish better swarm intelligent systems and a safer and optimised harmonious smart environment enabling cooperative FAGVs-SC automation systems. The challenges of urbanisation, if unmet urgently, would entail severe economic and environmental impacts. The integration of FAGVs with SC helps improve the sustainability of a city and the functional and efficient deployment of hand over wheels on robotized city roads with behaviour coordination. SC can enable the exploitation of the full potential of FAGVs with embedded centralised systems within SC with highly distributed systems in a concept of Automation of Everything (AoE). This article proposes a synergistic integrated FAGV-SC holistic framework - FAGVinSCF in which all the components of SC and FAGVs involving recent and impending technological advancements are moulded to make the transformation from today's driving society to future's next-generation driverless society smoother and truly make self-driving technology a harmonious part of our cities with sustainable urban development. Based on FAGVinSCF, a simulation platform is built both to model the varying penetration levels of FAGV into mixed traffic and to perform the optimal self-driving behaviours of FAGV swarms. The results show that FAGVinSCF improves the urban traffic flow significantly without huge changes to the traffic infrastructure. With this framework, the concept of Cooperative Intelligent Transportation Systems (C-ITS) is transformed into the concept of Automated ITS (A-ITS). Cities currently designed for cars can turn into cities developed for citizens using FAGVinSCF enabling more sustainable cities

Evaluating human decisions during time and actor independent variables

The Trolley Problem, a well known thought experiment comparing decisions during life or death circumstances, was applied by the Moral Machine. This gained 40 million decisions from millions of participants. Whilst accepted and praised for its success, investigation is available of participants position in the environment and the effect of time-pressure on the decisions made. To answer this, a web study was conducted to gain a quantitative understanding of participants likelihood to make life or death decisions under the effect of the independent variables via generalised estimating equation. The effects of which proved to be non-significant across both independent variables. Time-pressure showed self-sacrifice to be twice as likely when under time-pressure (B = 0.512, p = 0.012). This effect was studied via a quantitative and qualitative virtual reality study, understanding whether the significance is repeatable. The results indicate the opposite, showing regardless of the independent variable, participants are likely to sacrifice themselves. The explanation of the prior studies findings being concluded as 5% false positive in regards to significance. The implications of both studies provide validation into the Moral Machine’s results, showing the independent variables not chosen by the Moral Machine had little significance on participants decisions. This provides understanding around the development of a Trolley Problem algorithm in autonomous vehicles and the effects that would occur in the world. The research also provides a recommendation that research is required to understand the time taken to make a decision during both time and non-time pressure decisions. This would be to see if non-time pressure is being treated as such