IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 10, Issue 1 Ver. I (Jan – Feb. 2015), PP 94-102 www.iosrjournals.org DOI: 10.9790/1676-101194102 www.iosrjournals.org 94 | Page Automatic parking and platooning for electric vehicles redistribution in a car-sharing application Mohamed Marouf 1 , Evangeline Pollard 1 , Fawzi Nashashibi 1 1 RITS Project-Team, INRIA Paris-Rocquencourt, France Abstract: In car-sharing applications and during certain time slots, some parking stations become full whereas others are empty. To redress this imbalance, vehicle redistribution strategies must be elaborated. As automatic relocation cannot be in place, one alternative is to get a leader vehicle, driven by a human, which come to pick up and drop off vehicles over the stations. This paper deals with the vehicle redistribution problem among parking stations using this strategy and focusing on automatic parking and vehicle platooning. We present an easy exit parking controller and path planning based only on geometric approach and vehicle's characteristics. Once the vehicle exits the parking, it joins a platoon of vehicles and follows it automatically to go to an empty parking space. Keywords: Automatic Parking, Platooning, Car-sharing, Path Planning I. Introduction Sustainable mobility leads to limit individual properties and to increase resource sharing. This is particularly true and realistic concerning urban transportation means, where bikes, motorbikes, cars and any new urban transportation systems[1]can be easily shared due to the high concentration of people. In Paris, for instance, the trend is to develop self-service mobility services. With the bike sharing system Velib, comprising 14 000 bikes, 1200 stations and 225 000 subscribers, as well as the electric car-sharing system autolib, comprising 2000 vehicles, 1200 stations and 65 000 subscribers[2], Paris is definitively following this new mobility trend. Both Velib and autolib systems are conceived as multiple station shared vehicle systems (MSSVS)[3] for short local trips (home to workplace, or home to the closest station for instance). In these systems, a group of vehicles is distributed among fixed stations. With MSSVS, round trips can occur but one- way trips as well, leading to a complicated fleet management. Indeed, the number of vehicles per station can quickly become imbalanced depending on the rush time and on the location (living areas vs. commercial areas). There are frequent disparities between the availability of rental vehicle and the number of rental parking spaces. Relocation strategies are then useful to balance the number of vehicles and meet the demand. To solve this problem with the Velib system, operators manually displace more than 3000 bikes daily, corresponding to 3 % of the total fleet motion. For car-sharing system, relocation strategies are more difficult to implement. Various complicated strategies of relocation have been proposed in the past [4]: ride-sharing (two people travel in one vehicle to pick up another), equip vehicles with a hitch to tow another vehicle behind, using a scooter which will be towed back. However, all these strategies suffer from a lack of time and energy efficiency. On the other hand, even if the tendency is to go towards automation opening new automatic relocation strategies, a fully automatic relocation, implying the movement of vehicles traveling without a driver on open roads, looks difficult for legal reasons. One alternative would have to get a leader vehicle with a driver and to regulate the number of vehicles over stations using platooning. In that way, the leader vehicle would act as an agent which would pick up and drop off vehicles over the stations. In this article, we are not dealing with the problem of pickup and delivery which is largely tackled in the literature [5][6]. We describe the implementation of a new system dedicated to an easy relocation using automatic parking and platooning for an electric car sharing application. Both perception, planning, control and communication issues are tackled in this article. A special attention will be given to the control aspects, parking maneuver and platooning staying challenging issues. Many researches on parallel parking have been presented with different control approaches. These approaches can be divided into two categories: one based on stabilizing the vehicle to a target point, the other is based on path planning. Some controllers of the first group are based on Lyapunov function [7]where the function's parameters have to be hardly changed according to the free parking space. Other controllers are based on fuzzy logic [8], neuro-fuzzy control [9] and neural network [10]. These latter controllers need learning human skills which is limited and not easily extended to more general cases. The second group of controllers are based on path planning [11][12]. These controllers plan a geometric collision-free path to park (res. retrieve) a vehicle in (resp. from) a parking space. These controllers can demand heavy computations. For this reason, we present in this paper an easy way for path planning based on non-holonomic kinematic model of a vehicle.