Analysis and control of chaos for lateral dynamics of electric vehicles

In this paper, the nonlinear dynamic model of the lateral system for electric vehicles (EVs) is proposed. Different from the traditional steering system, a driver’s reaction model is introduced and meanwhile the disturbance caused by irregularities of road surface is also considered in this paper. Based on the integrated nonlinear dynamic equations, it shows that the stability of lateral system of EVs is closely related to the heading speed of the vehicle. The lateral system has a Hopf bifurcation when the vehicle heading speed equals a critical value, and then enters into chaos domain along with the increment of the vehicle heading speed. The unstable behaviors may make EVs spin and even turn over, which are quite harmful to the safety of EVs. As for this issue, a control method is proposed and implemented to protect the vehicle from spinning and thus improve the safety of EVs. The computer simulation is utilized in this paper to analyze nonlinear dynamics, as well as to validate the existence of chaotic motions and the feasibility of the control scheme. From the simulation results, it shows that the chaotic motions existing in the EV lateral dynamics can be suppressed by the proposed control method, and thus the corresponding cornering performance and safety are improved.published_or_final_versio

Cooperative controllers for highways based on human experience

The AUTOPIA program has been working on the development of intelligent autonomous vehicles for the last 10 years. Its latest advances have focused on the development of cooperative manœuvres based on communications involving several vehicles. However, so far, these manœuvres have been tested only on private tracks that emulate urban environments. The first experiments with autonomous vehicles on real highways, in the framework of the grand cooperative driving challenge (GCDC) where several vehicles had to cooperate in order to perform cooperative adaptive cruise control (CACC), are described. In this context, the main challenge was to translate, through fuzzy controllers, human driver experience to these scenarios. This communication describes the experiences deriving from this competition, specifically that concerning the controller and the system implemented in a Citröen C3

Dispositivo y procedimiento útil para el control de un automóvil, con ayuda de GPS y comunicaciones inalámbricas, que permite efectuar adelantamientos

Dispositivo y procedimiento útil para el control de un automóvil, con ayuda de GPS y comunicaciones inalámbricas, que permite efectuar adelantamientos. El dispositivo y procedimiento permite el control automático para realizar mediante un computador maniobras de adelantamiento en carretera, manejando los tres actuadores principales del vehículo, acelerador, freno y dirección sin intervención humana, basándose en información sensorial proveniente de sistemas GNSS, redes de comunicaciones inalámbricas Wi.Fi y la información de navegación del propio vehículo. La computación de control del sistema esta basada en algoritmos de lógica borrosa desarrollados por los autores.Peer reviewedConsejo Superior de Investigaciones Científicas (España)A1 Solicitud de patentes con informe sobre el estado de la técnic

Sähköbussin nopeuden ja ohjauskulman säätö edellä ajavan ajoneuvon liike-radan seuraamisessa

Buses face problems when the capacity of a bus is limited but it should be larger to be able to carry more passengers. The capacity of a bus is already increased to its maximum that is allowed by the infrastructure. The capacity of a bus line could be increased by driving buses more frequently but it would increase costs, that is unwanted. Costs could be reduced by driving buses as platoons consisting of two buses where only the first bus would be operated by a professional driver and the second would be driven autonomously. Autonomous driving requires longitudinal and lateral control of a vehicle. The follower bus should be able to follow the path driven by the leader bus precisely and avoid inter-vehicular collisions while still driving as close together as possible to indicate other traffic that they move as a platoon. Lateral control is usually divided into path following and direct following methods in the literature. Path following methods include obtaining the path of the leader vehicle and following of that path. Path following methods are usually accurate in terms of lateral error but are complex and require a lot of computational capacity. Direct following methods are easy to compute but they do not guarantee precise path following. A simulation model consisting of two identical buses was developed. One longitudinal controller and four lateral control laws were proposed. Longitudinal controller was designed to work also in tight turns which is not usually investigated. Lateral control laws proposed were geometrical in nature and required only input as the relative position of the leader bus. Therefore, they did not require inter-vehicular communication. Longitudinal controller worked well for initialization of the system with inter-vehicular distances from 2 to 10 m. It worked well in acceleration and deceleration tests when both buses were loaded similarly but failed to prevent collisions when follower bus was loaded more heavily than the leader. In lateral controller tests, Pure Pursuit and Modified Pure Pursuit methods were able to follow the leader producing following lateral errors: 0,8 m and 1,1 m (steady-state tests), 0,8 m and 0,7 m (u-turn maneuver) and 0,3 m/0,4 m and 0,4 m/0,5 m (double lane change maneuver, 5 m/s/10 m/s respectively). Spline Pursuit method showed oscillatory behavior and did not follow the leader well. Circular Pursuit method showed also oscillatory behavior and did not follow the leader well. However, it showed better performance than the Spline Pursuit. It remains to be studied whether Pure Pursuit or Modified Pure Pursuit can challenge more sophisticated path following methods.Linja-autojen matkustajakapasiteetti on rajallinen, mikä aiheuttaa ongelmia, sillä sen tulisi olla suurempi. Kapasiteetti on jo nostettu suurimmalle mahdolliselle tasolle, mitä nykyinen infrastruktuuri mahdollistaa. Linja-autolinjan kapasiteettia voisi nostaa ajamalla linja-autoja tiheämmin. Tämä kuitenkin johtaa suurempiin kustannuksiin. Kustannuksia voisi vähentää ajamalla linja-autoja kahden ajoneuvon jonoina, joissa ensimmäistä ajo-neuvoa ohjaisi ammattilaiskuljettaja ja toinen olisi autonomisesti ohjattu. Autonominen ajaminen vaatii ajoneuvon nopeuden ja ohjauskulman säätöä. Seuraajalinja-auton pitää pystyä seuraamaan johtajalinja-auton ajamaa ajouraa tarkasti ja välttää törmäämistä johtajaan. Linja-autojen välinen etäisyys on kuitenkin oltava riittävän pieni, jotta se viestisi muulle liikenteelle, että ajoneuvot ajavat jonona. Kirjallisuus jakaa ohjauskulman säädön yleensä ajouran seuraamiseen ja suoraan seuraamiseen. Ajouran seuraaminen koostuu johtaja-ajoneuvon ajouran saamisesta ja tämän uran seuraamisesta. Ajouran seuraamisen metodit ovat yleensä tarkkoja poikittaisen virheen suhteen, mutta ovat monimutkaisia ja vaativat paljon laskennallista kapasiteettia. Suoran seuraamisen metodit ovat laskennallisesti kevyitä, mutta eivät takaa tarkkaa ajouran seuraamista. Kahdesta identtisestä linja-autosta koostuva simulaatiomalli kehitettiin. Yksi nopeussäädin ja neljä ohjauskulman säätölakia esitettiin. Nopeussäädin suunniteltiin toimimaan myös tiukoissa käännöksissä, mitä ei ole yleensä tutkittu. Ohjauskulman säätölait perustuivat geometriseen päättelyyn ja ne tarvitsivat vain johtajalinja-auton suhteellisen asentotiedon. Säätölait eivät vaatineet ajoneuvojen välistä kommunikaatiota. Nopeussäädin toimi järjestelmän alustamisessa ajoneuvojen välisen etäisyyden ollessa 2-10 m. Se toimi hyvin kiihdytys- ja jarrutustesteissä, kun molemmat linja-autot olivat lastattu identtisellä kuormalla, mutta epäonnistui estämään törmäämisen, kun seuraajalinja-auto oli lastattu suuremmalla kuormalla kuin johtaja. Ohjauskulman säädön testeissä Pure Pursuit ja Modified Pure Pursuit pystyivät seuraamaan johtajaa seuraavilla poikittaissuuntaisilla virheillä: 0,8 m ja 1,1 m (steady-state-testit), 0,8 m ja 0,7 m (u-käännös) ja 0,3 m/0,4 m ja 0,4 m/0,5 m (kaksoiskaistanvaihto, 5 m/s/10 m/s vastaavasti). Spline Pursuit käyttäytyi värähtelevästi eikä seurannut johtajaa hyvin. Circular Pursuit käyttäytyi värähtelevästi eikä seurannut johtajaa hyvin, mutta kuitenkin paremmin kuin Spline Pursuit. Jää nähtäväksi pystyykö Pure Pursuit tai Modified Pure Pursuit haastamaan monimutkaisempia ajouran seuraamisen metodeja

Intelligent automatic overtaking system using vision for vehicle detection

There is clear evidence that investment in intelligent transportation system technologies brings major social and economic benefits. Technological advances in the area of automatic systems in particular are becoming vital for the reduction of road deaths. We here describe our approach to automation of one the riskiest autonomous manœuvres involving vehicles – overtaking. The approach is based on a stereo vision system responsible for detecting any preceding vehicle and triggering the autonomous overtaking manœuvre. To this end, a fuzzy-logic based controller was developed to emulate how humans overtake. Its input is information from the vision system and from a positioning-based system consisting of a differential global positioning system (DGPS) and an inertial measurement unit (IMU). Its output is the generation of action on the vehicle’s actuators, i.e., the steering wheel and throttle and brake pedals. The system has been incorporated into a commercial Citroën car and tested on the private driving circuit at the facilities of our research center, CAR, with different preceding vehicles – a motorbike, car, and truck – with encouraging results

Controladores borrosos para la dirección de vehículos autónomos en maniobras dentro de entornos urbanos

National audienceHasta la fecha, los sistemas de ayuda a la conducción desarrollados en el sector de la automoción se centran especialmente en el control de velocidad del vehículo. Sin embargo, sistemas que involucren el control (ya sea parcial o total) sobre la dirección del vehículo se encuentran todavía en fase experimental. Este trabajo está centrado en el diseño, desarrollo e implementación de un sistema de control lateral en cascada para vehículos autónomos reales, basado en controladores borrosos de alto nivel para maniobras en circuitos urbanos. Diferentes experimentos se han llevado a cabo en curvas de distinto radio y a diferentes velocidades (dentro de entornos urbanos), además, se han implementado dos nuevas maniobras: la marcha atrás y conducción en rotondas, mostrando un buen funcionamiento

Autonomous car fuzzy control modeled by iterative genetic algorithms

International audienceThe techniques of Soft Computing are recognized as having a strong learning and cognition capability as well as good tolerance to uncertainty and imprecision. These properties allow them to be applied successfully to Intelligent Transportation Systems (ITS), a broad range of diverse technologies that designed to answer many transportation problems. The unmanned control of the steering wheel is one of the most important challenges faced by researchers in this area. This paper presents a method of automatically adjusting a fuzzy controller to manage the steering wheel of a mass-produced vehicle. Information about the state of the car while a human driver is handling it is captured and used to search, via genetic algorithms, for the best fit of an appropriate fuzzy controller. Evaluation of the fuzzy controller will take into account its adjustment to the human driver's actions and the absence of abrupt changes in its control surface, so that not only is the route tracking good, but the drive is smooth and comfortable for the vehicle's occupants

Trajectory generator for autonomous vehicles in urban environments

International audienceNowadays, some developments in the vehicle industry permit a safe and comfortable driving. However, several manufactures and research groups are still working in the improvement of the control strategies and path smoothing algorithms. In this paper, a new trajectory generation approach for autonomous vehicles in urban scenarios, considering parametric equations, is proposed. An algorithm that considers Bezier curves and circumference parametric equations for a real vehicle, specifically in roundabout and urban intersections is presented. This approach is generated in real time and can be adapted to dynamic changes in the route. A smooth trajectory generator computationally efficient and easily implementable is proposed. Moreover, this new trajectory generator reduces the control actions, generated with to a fuzzy controller. Some trials have been performed in an urban circuit with promising performance