Proceeding of: 15th International Symposium on Advanced Vehicle Control (AVEC '22), September 12-15, 2022, Kanagawa, JapanThe novelty of this paper is an Event-Triggered LPV Output-Feedback H∞ controller that generates a steering control signal to follow the road, an acoustic sensor based AEB-P system which avoids vehicle collision with pedestrians and a speed controller based on the curvature of the path. The validation of the proposed system is done through simulation tests with CarSim®This work was supported by the FEDER/Ministry of Science and Innovation - Agencia Estatal de Investigación (AEI) of the Government of Spain through the project [RTI2018-095143-B-C21]

López Boada, Beatriz

López Boada, María Jesús

Viadero Monasterio, Fernando

English

Universidad Carlos III de Madrid e-Archivo

       This is the paper presented at:  15th International Symposium on Advanced Vehicle Control (AVEC '22), September 12-15, 2022, Kanagawa, Japan  Viadero-Monasterio, Fernando, et al. Autonomous path following and emergency braking control for intelligent vehicles using low cost devices     © The authors 2022 AVEC’22Autonomous Path Following and Emergency Braking Controlfor Intelligent Vehicles Using Low Cost DevicesFernando Viadero-Monasterio, M. Jimenez-Salas,  B. L. Boada, M. J. L. BoadaMechanical Engineering Department, Institute for Automotive Vehicle Safety (ISVA) ,Universidad Carlos III de Madrid, Avda. de la Universidad 30, Leganés, 28911 Madrid, SpainE-mail: fviadero@ing.uc3m.esThe novelty of this paper is an Event-Triggered LPV Output-Feedback H∞ controller thatgenerates a steering control signal to follow the road, an acoustic sensor based AEB-P systemwhich avoids vehicle collision with pedestrians and a speed controller based on the curvature ofthe path. The validation of the proposed system is done through simulation tests with CarSim®.- Autonomous Driving Systems- Advanced Driver Assistance Systems- Sensors and Actuators1. INTRODUCTIONAutonomous driving is one of the most promisingresearch topics in road transport and is set to solvemany problems related to traffic efficiency, accessibilityand safety. Although years of research still lie ahead toachieve the full replacement of human drivers, newapproaches to autonomous driving strategy are beingdeveloped to improve road safety and humanconfidence in autonomous driving.An autonomous vehicle gathers sensors andactuators, all connected through a communicationnetwork, in order to drive autonomously. Pathgeneration and path-following control are the basis ofautomated driving systems. When the path can beknown in advance and only a path-following algorithmis needed, the problem is known as path-tracking, whichis easier to compute; whereas when the trajectory has tobe calculated, the problem has to include apath-planning algorithm and is known as path-planning.After the desired paths are obtained by path-generationalgorithms, a path-following method can be defined totrack the generated paths.Obstacle detection technologies are a keydevelopment point in autonomous vehicles. Togetherwith automatic emergency braking (AEB) systems,reliability of autonomous vehicles and safety ofvulnerable road users, such as pedestrians, can beimproved. Although systems like these are alreadybeing integrated in commercial vehicles, hightechnology costs are still a problem to address in orderto achieve a large fleet of vehicles at an affordable pricewith advanced safety systems.The novelty of this work is the development of aLMI-Based H∞ output-feedback path-followingcontroller that keeps the vehicle on a previously knownpath, and an AEB-P, which avoids vehicle collision withpedestrians. This control system is presented forautomotive use under low-cost or already built insystems. The algorithm is validated through asimulation using the software CarSim®, which is one ofthe preferred programs for studying vehicle dynamicsamong automotive researchers.2. H∞ OUTPUT-FEEDBACK PATH-FOLLOWINGCONTROLLERThe robust event-triggered LPV output-feedbackpath following controller design is presented throughthis section.2.1 Vehicle modelThe mathematical model followed for this work isdescribed on [1] and depicted in Figure 1Fig. 1. Yaw motion model [1]AVEC'22AVEC’22Vehicle planar motion is assumed as the roadcondition studied for this work has neglectable heightdifferences along the path. The ground vehicle ismodeled as a rigid body with longitudinal motion, lat-eral motion and yaw motion. The Pitch and roll motionare ignored.2.2 H∞ Controller designFor the design of the proposed H∞ output-feedbackpath-following controller, the control input is defined as(1)𝑢(𝑡) = 𝐾(𝜌)ỹ(𝑡)where is the control gain matrix to be designed,𝐾(𝜌)which is dependent to the linear time varying parametervector ; is the delayed plant𝜌 = [𝑣𝑥 1/𝑣𝑥] ỹ(𝑡)measurement, which is sent by an event triggeringmechanism every time the observed measurement fromthe plant changes notoriously; this reduces the networkusage and lowers the amount of resources involved inthe communication process [2]. Taking the definition in(1), the mathematical model of the closed-loop systemis expressed asẋ(𝑡) = 𝐴(𝜌)𝑥(𝑡) + 𝐵𝑢𝐾(𝜌)ỹ(𝑡) + 𝐵𝑑⍵(𝑡)(2)𝑦(𝑡) = 𝐶1𝑥(𝑡); 𝑧(𝑡) = 𝐶2𝑥(𝑡)where y(t) is the plant measurement and z(t) is thecontrolled output.The closed-loop system in eq. (2) has an H∞performance level γ under zero initial condition if thefollowing inequality is satisfied(3)|| 𝑧𝑇(𝑡)𝑧(𝑡)||2< γ||⍵𝑇(𝑡)⍵(𝑡)||2In order to ensure system stability, aLyapunov-Krasovskii functional is chosen as in [3],where high order functionals are considered to analyzehow the system is affected by event delays.After the restrictions of the system are set, thecontroller gain matrix is designed under a Linear MatrixInequality optimization problem. For this issue, theMatlab® Robust Control Toolbox is employed due to itssimplicity; however, non-commercial software such asYalmip can be considered as an alternative.3. AEB-P CONTROL SYSTEM WITH ACOUSTICSENSORSThis section presents the proposed three-stagecascade braking AEB-P control algorithm.3.1 Multiple beam acoustic sensor arrayPedestrian detection is achieved through a 7-beamacoustic sensor system that can detect pedestrians up to20 meters ahead of the vehicle within the normal widthof a road [4].The use of acoustic technology for pedestriandetection improves the probability of detection in poorvisibility conditions compared to other technologiesbased on light propagation, such as LIDAR or RADARsystems, and also costs are reduced.3.2 AEB-P control algorithmThe designed AEB-P control system is based on theTTC (Time To Collision) calculation , which representsthe remaining time until a collision between the vehicleand an object (pedestrian) occurs if both the headingand the relative speed between them remain constant.TTC is calculated as follows:,𝑇𝑇𝐶 =𝑑𝑟𝑣𝑟where and are the relative distance and velocity𝑑𝑟𝑣𝑟between the vehicle and the nearest pedestrian. TheTTC value is compared with the stopping times thatdetermine the activation of one of the two stages ofpartial braking or full braking, as shown in Figure 2.The stopping time is the travel time elapsed since adeceleration is applied until the vehicle, with𝑎𝑏𝑟𝑎𝑘𝑒initial velocity , comes to a complete stop:𝑣𝑥τ𝑠𝑡𝑜𝑝=𝑣𝑥𝑎𝑏𝑟𝑎𝑘𝑒For each of the braking stages, a differentdeceleration reference is used for the automatic brakecontrol. The values are shown in Table 1.Deceleration reference Value (m/s2)ab1 3.8ab2 5.3abfull 9.8Table 1. Deceleration reference valuesFig. 2. Operating representation of AEB-P system4. RESULTSThe path defined for this test is presented in Figure3. Since the test path is known in advance, vehiclespeed is controlled depending on the path curvature asdone in previous works [5].The results of the vehicle lateral error to path withthe speed and steering controller are depicted in Figure4. The maximum lateral error to the path appears on thesharpest turn.AVEC'22AVEC’22Once a pedestrian is detected, AEB-P systemtriggers to avoid collision with the pedestrian bydiminishing vehicle speed. Figure 5 illustrates thelongitudinal dynamics of the vehicle in more detailwhen the AEB system is triggered by pedestriandetection. In this particular case, the pedestrian isdetected at a distance of 20 meters from the front of thevehicle by sensor number 3 and, eventually, by sensornumber 2. Given the velocity of the vehicle and therelative distance to the pedestrian, the AEB controllerdecides to apply the second stage of partial braking tofinally stop the vehicle before a collision occurs.Fig. 3. Test pathFig. 4. Lateral error to path and vehicle speedFig. 5. Vehicle speed, deceleration and relative distancewith nearest pedestrian in an AEB activation5. CONCLUSIONThis paper presents a control system forautonomous vehicles in which the designedpath-following algorithm can track any path while theproposed AEB-P detects and avoids possible collisionswith pedestrians under a low cost architecture.For the path-following problem, a discreteevent-triggered mechanism has been defined todetermine when the measurements from the plantshould be transmitted over the network in order toreduce the Transmission Rate. A Lyapunov–Krasovskiifunctional analyzes the stability criterion for theclosed-loop system, taking into account the existence ofnetwork delays. LMI based conditions have beenpresented in order to find a feasible controller, whichguarantees that the closed-loop presents anasymptotically stable behavior with a prescribed 𝐻∞disturbance attenuation level. The performance of theproposed controller has been tested with CarSim®,which works with high-order non-linear vehicle modelsin order to have a more reliable approach to a realsystem.The AEB-P control algorithm analyzes whether acollision may occur and computes a deceleration valuein order to reduce the vehicle speed and stop the carbefore reaching any possible obstacle while maintaininggood levels of comfort. It is worth mentioning that theprocess behind the detections of the multiple beamacoustic sensor array has been neglected as the objectivein this work is to study the performance of the AEB-Palgorithm in different detection scenarios. Work is inprogress and tests with the real sensor array mounted ina vehicle are being made in order to evaluate the realperformance of the controller and make the necessaryadaptations to deal with the noise, uncertainties andpossible false detections of the acoustic system.Future works may include path-generationalgorithms to consider performing an emergencymaneuver whenever the vehicle approaches an obstacleon more complex road conditions. In addition to this, itis desirable to analyze system failures during thecontroller design process to achieve a more robustsolution. The ultimate goal of this project is to evaluatethe performance of the proposed system on a realvehicle.ACKNOWLEDGEMENTSThis work was supported by the FEDER/Ministry ofScience and Innovation - Agencia Estatal deInvestigacion (AEI) of the Government of Spainthrough the project [RTI2018-095143-B-C21].AVEC'22AVEC’22REFERENCES[1] Chen, Y., Zhang, Y., & Zhang, F. (2021).Personalized path generation and robust H∞output‐feedback path following control forautomated vehicles considering driving styles. IETIntelligent Transport Systems, 15(12), 1582-1595.[2] Viadero-Monasterio, F., Boada, B. L., Boada, M. J.L., & Díaz, V. (2022). H∞ dynamic output feedbackcontrol for a networked control active suspensionsystem under actuator faults. Mechanical Systemsand Signal Processing, 162, 108050.[3] Redondo, J. P., Boada, B. L., & Díaz, V. (2021).LMI-Based H∞ Controller of Vehicle Roll StabilityControl Systems with Input and Output Delays.Sensors, 21(23), 7850.[4] Izquierdo, A., del Val, L., & Villacorta, J. J. (2021).Feasibility of Using a MEMS Microphone Array forPedestrian Detection in an Autonomous EmergencyBraking System. Sensors, 21(12), 4162.[5] Marin-Plaza, Pablo, David Yagüe, Francisco Royo,Miguel Á. de Miguel, Francisco M. Moreno,Alejandro Ruiz-de-la-Cuadra, FernandoViadero-Monasterio, Javier Garcia, José L. SanRoman, and José M. Armingol. 2021. ProjectARES: Driverless Transportation System.Challenges and Approaches in an UnstructuredRoad. Electronics. 2021; 10(15):1753.AVEC'22View publication stats

Autonomous path following and emergency braking control for intelligent vehicles using low cost devices

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Autonomous path following and emergency braking control for intelligent vehicles using low cost devices

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