动态矩阵和模型预测控制的半自动驾驶汽车(自动控制论文)

Dhaval Shroff1, Harsh Nangalia1, Akash Metawala1, Mayur Parulekar1, Viraj Padte1

Research and Innovation Center

Dwarkadas J. Sanghvi College of Engineering

Mumbai, India.

dhaval92shroff@; mvparulekar@

Abstract—Dynamic matrix and model predictive control in a car aims at vehicle localization in order to avoid collisions by providing computational control for driver assistance whichprevents car crashes by taking control of the car away from the driver on incidences of driver’s negligence or distraction. This paper provides ways in which the vehicle’s position with reference to the surrounding objects and the vehicle’s dynamic movement parameters are synchronized and stored in dynamic matrices with samples at regular instants and hence predict the behavior of the car’s surrounding to provide the drivers and the passengers with a driving experience that eliminates any reflex braking or steering reactions and tedious driving in traffic conditions or at junctions.It aims at taking corrective action based on the feedback available from the closed loop system which is recursively accessed by the central controller of the car and it controls the propulsion and steeringand provides a greater restoring force to move the vehicle to a safer region.Our work is towards the development of an application for the DSRC framework (Dedicated Short Range Communication for Inter-Vehicular Communication) by US Department of Traffic (DoT) and DARPA (Defense Advanced Research Projects Agency) and European Commission- funded Project SAVE-U (Sensors and System Architecture for Vulnerable road Users Protection) and is a step towards Intelligent Transportation Systems such as Autonomous Unmanned Ground and Aerial Vehicular systems.

Keywords-Driver assist, Model predictive control, Multi-vehicle

co-operation, Dynamic matrix control, Self-mapping

I.INTRODUCTION

Driver assist technologies aim at reducing the driver stress and fatigue, enhance his/her vigilance, and perception of the environment around the vehicle. It compensates for the driver’s ability to react [6].In this paper, we present experimental results obtained in the process of developing a consumer car based on the initiative of US DoT for the need for safe vehicular movement to reduce fatalities due to accidents [5]. We aim at developing computational assist for the car using the surrounding map data obtained by the LiDAR (Light Detection and Ranging) sensors which is evaluated and specific commands are issued to the vehicle’s propellers to avoid static and dynamic obstacles. This is also an initiative by the Volvo car company [1] where they plan to drive some of these control systems in their cars and trucks by 2020 and by General Motors, which aims to implement semi-autonomous control in cars for consumers by the end of this decade [18].Developments in wireless and mobile communication technologies are advancing methods for ex- changing driving information between vehicles and roadside infrastructures to improve driving safety and efficiency [3]. We attempt to implement multi-vehicle co-operative communication using the principle of swarm robotics, which will not only prevent collisions but also define specific patterns, which the nearby cars can form and pass through any patch of road without causing traffic jams. The position of the car and the position of the obstacles in its path, static or moving, will be updated in real time for every sampling point and stored in constantly updated matrices using the algorithm of dynamic matrix control. Comparing the sequence of previous outputs available with change in time and the inputs given to the car, we can predict its non-linear behavior with the help of model predictive control. One of the advantages of predictive control is that if the future evolution of the reference is known priori, the system can react before the change has effectively been made, thus avoiding the effects of delay in the process response [16]. We propose an approach in which human driving behavior is modeled as a hybrid automation, in which the mode is unknown and represents primitive driving dynamics such as braking and acceleration. On the basis of this hybrid model, the vehicles equipped with the cooperative active safety system estimate in real-time the current driving mode of non-communicating human-driven vehicles and exploit this information to establish least restrictive safe control actions [13].For each current mode uncertainty, a mode dependent dynamic matrix is constructed, which determines the set of all continuous states that lead to an unsafe configuration for the given mode uncertainty. Then a feedback is obtained for different uncertainties and corrective action is applied accordingly [7].This ITS (Intelligent Transport System) -equipped car engages in a sort of game-theoretic decision, in which it uses information from its onboard sensors as well as roadside and traffic-light sensors to try to predict what the other car will do, reacting accordingly to prevent a crash.When both cars are ITS-equipped, the “game” becomes a cooperative one, with both cars communicating their positions and working together to avoid a collision [19]. The focus is to improve the reaction time and the speed of communication along with more accurate vehicle localization. In this paper, we concentrate on improving vehicle localization using model predictive control and dynamic matrix control algorithm by sampling inputs of the car such as velocity, steering frame angle, self-created maps

Dynamic Matrix and Model Predictive Control for a Semi-Auto Pilot Car