Driving automation: Learning from aviation about design philosophies

Full vehicle automation is predicted to be on British roads by 2030 (Walker et al., 2001). However, experience in aviation gives us some cause for concern for the 'drive-by-wire' car (Stanton and Marsden, 1996). Two different philosophies have emerged in aviation for dealing with the human factor: hard vs. soft automation, depending on whether the computer or the pilot has ultimate authority (Hughes and Dornheim, 1995). This paper speculates whether hard or soft automation provides the best solution for road vehicles, and considers an alternative design philosophy in vehicles of the future based on coordination and cooperation

The Evaluation of Route Guidance Systems

BACKGROUND We were commissioned by the Transport and Road Research Laboratory to: "collaborate with the German government and their representatives who are responsible for conducting the LISB trial in Berlin in order to produce an agreed methodology, which is acceptable in both Germany and the UK, for assessing the automatic route guidance systems which will be provided in Berlin and London." The brief suggested a number of aspects to be included, and required detailed proposals, timescales and costs for implementation in London. 1.1.2 The background to the brief lies in decisions to introduce pilot automatic route guidance systems in the two cities. The principles of the systems are similar, and have been described in detail elsewhere (Jeffery, 1987). In brief, they involve : (i) a central computer which retains information on a specified road network, which is updated using real time information from the equipment users; (ii) infra red beacons at selected junctions which transmit information to equipped vehicles and receive information from those vehicles; (iii) in-vehicle equipment which includes a dead-reckoning system for position finding, a device for requesting guidance and specifying the destination, a micro-computer which selects the optimal route, and a display which indicates when a turn is required on the main network, and the compass direction and distance to the final destination; iv) transmission from the equipped vehicles of origin, requested destination, links used since passing the last beacon and, for each link, the time of entry and departure and time spent delayed. It is this travel time information which is used to update the central computer's knowledge of the best routes. (Continues..

Modelling Safety-Related Driving Behaviour - the Impact of Parameter Values

Traffic simulation models make assumptions about the safety-related behaviour of drivers. These assumptions may or may not replicate the real behaviour of those drivers who adopt seemingly unsafe behaviour, for example running red lights at signalised intersections or too closely following the vehicles in front. Such behaviour results in the performance of the system that we observe but will often result in conflicts and very occasionally in accidents. The question is whether these models should reflect safe behav- iour or actual behaviour. Good design should seek to enhance safety, but is the safety of a design neces- sarily enhanced by making unrealistically optimistic assumptions about the safety of drivers behaviour? This paper explores the questions associated with the choice of values for safety-related parameters in simulation models. The paper identifies the key parameters of traffic simulation models and notes that sev- eral of them have been derived from theory or informed guesswork rather than observation of real behav- iour and that, even where they are based on observations, these may have been conducted in circumstances quite different to those which now apply. Tests with the micro-simulation model DRACULA demonstrate the sensitivity of model predictions—and perhaps policy decisions—to the value of some of the key param- eters. It is concluded that, in general, it is better to use values that are realistic-but-unsafe than values that are safe-but-unrealistic. Although the use of realistic-but-unsafe parameter values could result in the adop- tion of unsafe designs, this problem can be overcome by paying attention to the safety aspects of designs. The possibility of using traffic simulation models to culties involved in doing so are discussed

Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines

Responsible Autonomy

As intelligent systems are increasingly making decisions that directly affect society, perhaps the most important upcoming research direction in AI is to rethink the ethical implications of their actions. Means are needed to integrate moral, societal and legal values with technological developments in AI, both during the design process as well as part of the deliberation algorithms employed by these systems. In this paper, we describe leading ethics theories and propose alternative ways to ensure ethical behavior by artificial systems. Given that ethics are dependent on the socio-cultural context and are often only implicit in deliberation processes, methodologies are needed to elicit the values held by designers and stakeholders, and to make these explicit leading to better understanding and trust on artificial autonomous systems.Comment: IJCAI2017 (International Joint Conference on Artificial Intelligence

Modelling transport energy demand : a socio-technical approach

Peer reviewedPostprin

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2

Ethical and Social Aspects of Self-Driving Cars

As an envisaged future of transportation, self-driving cars are being discussed from various perspectives, including social, economical, engineering, computer science, design, and ethics. On the one hand, self-driving cars present new engineering problems that are being gradually successfully solved. On the other hand, social and ethical problems are typically being presented in the form of an idealized unsolvable decision-making problem, the so-called trolley problem, which is grossly misleading. We argue that an applied engineering ethical approach for the development of new technology is what is needed; the approach should be applied, meaning that it should focus on the analysis of complex real-world engineering problems. Software plays a crucial role for the control of self-driving cars; therefore, software engineering solutions should seriously handle ethical and social considerations. In this paper we take a closer look at the regulative instruments, standards, design, and implementations of components, systems, and services and we present practical social and ethical challenges that have to be met, as well as novel expectations for software engineering.Comment: 11 pages, 3 figures, 2 table