A Full-fledge Simulation Framework for the Assessment of Connected Cars

Abstract Intelligent Transport Systems (ITS) have emerged as an integral part of smart cities, providing increased ease of mobility as well as efficiency and safety in vehicular traffic. Given its wide array of applications, ITS has also become a multidisciplinary field of work where vehicular communications, traffic control, ADAS (Advance Driver Assistance System) sensors, and vehicle dynamics have all to be accounted for. The study of such diverse aspects makes the evaluation of new ITS approaches, algorithms, and protocols not a small feat. For this reason, the availability of an effective, scalable, and comprehensive tool for the investigation and virtual validation of new ITS solutions is paramount. In this work, we present a simulation framework, called CoMoVe (Communication, Mobility, Vehicle dynamics), that effectively addresses the above need, as it enables the virtual validation of innovative solutions for vehicles that are both connected and equipped with ADAS sensors. Our framework encapsulates the important attributes of vehicle communication, road traffic, and dynamics into a single environment, by combining the strengths of different simulators. CoMoVe finds its use to evaluate the impact of vehicle connectivity, while imposing causality on vehicle dynamics and mobility. Such an assessment can greatly facilitate the development of control systems, algorithms, and protocols for real-world ITS

An evolutionary approach to the optimisation of autonomous pod distribution for application in an urban transportation service

For autonomous vehicles (AVs), which when deployed in urban areas are called “pods”, to be used as part of a commercially viable low-cost urban transport system, they will need to operate efficiently. Among ways to achieve efficiency, is to minimise time vehicles are not serving users. To reduce the amount of wasted time, this paper presents a novel approach for distribution of AVs within an urban environment. Our approach uses evolutionary computation, in the form of a genetic algorithm (GA), which is applied to a simulation of an intelligent transportation service, operating in the city of Coventry, UK. The goal of the GA is to optimise distribution of pods, to reduce the amount of user waiting time. To test the algorithm, real-world transport data was obtained for Coventry, which in turn was processed to generate user demand patterns. Results from the study showed a 30% increase in the number of successful journeys completed in a 24 hours, compared to a random distribution. The implications of these findings could yield significant benefits for fleet management companies. These include increases in profits per day, a decrease in capital cost, and better energy efficiency. The algorithm could also be adapted to any service offering pick up and drop of points, including package delivery and transportation of goods

On the Experimental Evaluation of Vehicular Networks: Issues, Requirements and Methodology Applied to a Real Use Case

One of the most challenging fields in vehicular communications has been the experimental assessment of protocols and novel technologies. Researchers usually tend to simulate vehicular scenarios and/or partially validate new contributions in the area by using constrained testbeds and carrying out minor tests. In this line, the present work reviews the issues that pioneers in the area of vehicular communications and, in general, in telematics, have to deal with if they want to perform a good evaluation campaign by real testing. The key needs for a good experimental evaluation is the use of proper software tools for gathering testing data, post-processing and generating relevant figures of merit and, finally, properly showing the most important results. For this reason, a key contribution of this paper is the presentation of an evaluation environment called AnaVANET, which covers the previous needs. By using this tool and presenting a reference case of study, a generic testing methodology is described and applied. This way, the usage of the IPv6 protocol over a vehicle-to-vehicle routing protocol, and supporting IETF-based network mobility, is tested at the same time the main features of the AnaVANET system are presented. This work contributes in laying the foundations for a proper experimental evaluation of vehicular networks and will be useful for many researchers in the area.Comment: in EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 201

A new model-free design for vehicle control and its validation through an advanced simulation platform

A new model-free setting and the corresponding "intelligent" P and PD controllers are employed for the longitudinal and lateral motions of a vehicle. This new approach has been developed and used in order to ensure simultaneously a best profile tracking for the longitudinal and lateral behaviors. The longitudinal speed and the derivative of the lateral deviation, on one hand, the driving/braking torque and the steering angle, on the other hand, are respectively the output and the input variables. Let us emphasize that a "good" mathematical modeling, which is quite difficult, if not impossible to obtain, is not needed for such a design. An important part of this publication is focused on the presentation of simulation results with actual and virtual data. The actual data, used in Matlab as reference trajectories, have been obtained from a properly instrumented car (Peugeot 406). Other virtual sets of data have been generated through the interconnected platform SiVIC/RTMaps. It is a dedicated virtual simulation platform for prototyping and validation of advanced driving assistance systems. Keywords- Longitudinal and lateral vehicle control, model-free control, intelligent P controller (i-P controller), algebraic estimation, ADAS (Advanced Driving Assistance Systems).Comment: in 14th European Control Conference, Jul 2015, Linz, Austria. 201

Fuzzy logic control for energy saving in autonomous electric vehicles

Limited battery capacity and excessive battery dimensions have been two major limiting factors in the rapid advancement of electric vehicles. An alternative to increasing battery capacities is to use better: intelligent control techniques which save energy on-board while preserving the performance that will extend the range with the same or even smaller battery capacity and dimensions. In this paper, we present a Type-2 Fuzzy Logic Controller (Type-2 FLC) as the speed controller, acting as the Driver Model Controller (DMC) in Autonomous Electric Vehicles (AEV). The DMC is implemented using realtime control hardware and tested on a scaled down version of a back to back connected brushless DC motor setup where the actual vehicle dynamics are modelled with a Hardware-In-the-Loop (HIL) system. Using the minimization of the Integral Absolute Error (IAE) has been the control design criteria and the performance is compared against Type-1 Fuzzy Logic and Proportional Integral Derivative DMCs. Particle swarm optimization is used in the control design. Comparisons on energy consumption and maximum power demand have been carried out using HIL system for NEDC and ARTEMIS drive cycles. Experimental results show that Type-2 FLC saves energy by a substantial amount while simultaneously achieving the best IAE of the control strategies tested