Simulation of Mixed Critical In-vehicular Networks

Future automotive applications ranging from advanced driver assistance to autonomous driving will largely increase demands on in-vehicular networks. Data flows of high bandwidth or low latency requirements, but in particular many additional communication relations will introduce a new level of complexity to the in-car communication system. It is expected that future communication backbones which interconnect sensors and actuators with ECU in cars will be built on Ethernet technologies. However, signalling from different application domains demands for network services of tailored attributes, including real-time transmission protocols as defined in the TSN Ethernet extensions. These QoS constraints will increase network complexity even further. Event-based simulation is a key technology to master the challenges of an in-car network design. This chapter introduces the domain-specific aspects and simulation models for in-vehicular networks and presents an overview of the car-centric network design process. Starting from a domain specific description language, we cover the corresponding simulation models with their workflows and apply our approach to a related case study for an in-car network of a premium car

Governance in niche development for a transition to a new mobility regime

Urban mobility is a difficult sustainability challenge; measures to reduce transport impacts produce only marginal reductions in overall energy use and CO2 emissions. Even fuel switch to electric vehicles and measures to manage traffic produce insufficient improvements. Seeking transport sustainability within the existing socio-technical regime involves policy approaches for dense cities to provide high-capacity, corridor-based public transport, expecting people to arrange their lives around such transport systems. Yet this socio-technical regime ill-fits modern mobility needs. The reluctance to use public transport stems much from this 150 year old regime configuration. The social-technical landscape has shifted significantly: travel patterns are increasingly dispersed in space and time – not funnelled into traditional corridor peak-hour movements. The key is not getting people to return to travel patterns of 100 years ago, but in a transition to a socio-technical transport regime that delivers sustainability compatible with the 21st century social-technical landscape. An opportunity may be emerging for socio-technical configurations in niche environments to effect transitions to alternate mobility futures. Autonomous vehicles are rapidly approaching market application. Since 2011, small autonomous pods have operated on segregated tracks at Heathrow Airport. In 2014 a similar system opened at the Suncheon Bay tourist area in South Korea. Since 2011 there have been public street trials of autonomous vehicles in the USA and in 2015 they became street legal in the UK. The Milton Keynes (MK) ‘Pathfinder’ project focuses on two-seat pods which do not need segregated tracks, but will run on cycleways and footpaths, mixing with cyclists and pedestrians. Trials will start in 2015, on short distance links from the railway station to destinations in Central Milton Keynes. This project forms part of the wider Milton Keynes Future Cities Programme and Open University-led MK:Smart project. This paper draws on these trials in MK to show through case study research how autonomous vehicles applications are moving beyond protected niches and, along with other developments, hold the potential to stimulate a major transition in public transport systems. The vehicles are small and each journey is individual to the passenger(s). Services do not run along corridor routes, like buses and trams, but are based on alternate rule-sets to the existing regime with individual journeys customised for each user. Such developments may therefore stimulate transition to totally different sorts of public transport systems and ultimately, socio-technical mobility regimes, by offering much more to users than any corridor system can provide. Rather than people adjusting their behaviour to bus routes, schedules and operating times, they travel directly, whenever they want, on services running 24/7. Thus these new regimes could be more compatible with lifestyle and economic trends that comprise 21st century socio-technical landscapes. As such, they provide credible alternatives to the private car, and so hold potential to deliver major sustainability gains. But such transitions face major challenges from entrenched actors within the existing regime. Taxis, minicabs and bus operators would be threatened. If the Uber cab app is being blocked by incumbent actors, they look likely to be powerful opponents of autonomous vehicle based cab services. However, MK provides an interesting innovation context where there are several overlapping smart transport niches in different stages of development. As well as autonomous pods, demand responsive minibuses are planned and inductive changed electric buses are in service. If these projects build links to each other (niche accumulation), demonstrate economic value and reproduced beyond their original experimental spaces (niche proliferation), there is potential for them to overcome incumbent resistance. In Milton Keynes, these processes could be getting close to reaching critical mass, opening up the possibility of moving closer to radical regime transitions

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV, in prep. for journal submission. In V3, we add a proof that the socially-optimal solution can be enforced as a general equilibrium, a privacy-preserving distributed optimization algorithm, a description of the receding-horizon implementation and additional numerical results, and proofs of all theorem

Transport poverty meets the digital divide : accessibility and connectivity in rural communities

Peer reviewedPublisher PD

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV and accepted by TCNS. In Version 4, the body of the paper is largely rewritten for clarity and consistency, and new numerical simulations are presented. All source code is available (MIT) at https://dx.doi.org/10.5281/zenodo.324165

All roads lead to the places of your interest: An on-demand, ride-sharing visitor transport service

Successful visitor transport within large tourist sites should balance visitor experience and operating costs. Inspired by the model of sharing economy, we design a “user-centered” intelligent visitor transport system to improve the efficiency and quality of experience of transport service in large tourist sites. The system’s core approach is a three-stage heuristic model based on Pareto optimality. Results of the proposed service indicate a drastic reduction of visitor delay time and an improvement in energy efficiency. The proposed scheduling schemes for organizers are more diversified and adaptable than the existing service

Towards a cyber physical system for personalised and automatic OSA treatment

Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database