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 Padte1Research and Innovation CenterDwarkadas J. Sanghvi College of EngineeringMumbai, 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-vehicleco-operation, Dynamic matrix control, Self-mappingI.INTRODUCTIONDriver 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 mapsDynamic Matrix and Model Predictive Control for a Semi-Auto Pilot Carrecursively and use them to control the vehicle’s propulsion.II.I NTEGRATING LANE DETECTION, VEHICLE LOCALIZATION AND VEHICLE LANE DEPARTURE MONITORINGWhenever a car is travelling on the road, it requires both longitudinal and lateral control to avoid collision with the other vehicles [9]. The camera sensor installed at the front end of the car can detect the white lines of the lanes using image processing and position the vehicle between the two lines. The vehicle’s heading angle on the road is adjusted based on the directions that the lane follows. However, this system exhibits measurement accuracy only locallycompared to Inertial Navigation Systems. So we combine the two systems for better results by obtaining the road map from the navigation system and locally maintaining the vehicle’s position within a lane using the camera sensor. It transforms the position and orientation data into a global reference using a map of the environment and then estimates localization parameters using a particle filter [14]. The vehicle’s orientation and propulsion parameters are recursively updated and with reference to the global mapping obtained, the vehicle control system creates a localized map of the surrounding for advanced precision. This map is then used for constant lane detection and restores the vehicle back to its lane in case lane change is not possible. If there is unsupervised lane departure, the vehicle’s auto pilot system takes control of the car and steers the car back to its lane. When humans perform a lane change, the profile of the lane change remains rather uniform under variety of circumstances. However, when virtual forces take control, it depends on the range and range rate to target vehicles. When this autonomous operation is performed, the factors such as the distance and speed of the car, which is approaching the host in that particular lane, or the speed and distance of the car in front, which is to be overtaken, are considered. Also, the behavior of the cars behind and in front are observed whether their speeds are changing or they are steering in an attempt to change the lane themselves, etc. The distance and speed of the next car that is approaching in the lane that the host decides to enter is also checked to avoid unnecessary lane changing and prevent collision. After evaluating these conditions, options are prioritized and the safest is chosen. If the overtaking or lane changing is taking place between two cars which are ITS-equipped, the car approaching with a higher speed can send a command to the car in front to either increase its speed or move to another lane and give way for overtaking if the circumstances for lane changing are favorable. Also, in case of lane departure by another vehicle adjacent to our ITS-equipped car, it can perform corrective anti-collision reaction by slowing down and allowing that vehicle to enter the lane or speeding up and crossing the patch of movement of the adjacent vehicle or following the same movement as that vehicle by going parallel in case of a diversion or obstacle in its course depending on the priority assigned by the model predictive trained controller of our car. Instead of assigning algorithms to our car, we plan to let the car’s control system constantly learn from the reaction the driver gives during different instances of driving. This learning procedure is carried out before delivering the car to the user from the factory and it is even continued during the normal course of driving. This makes the car adaptive to all permutations and combinations of conditions that can exist and the feedback obtained is stored and used in the improvement of systems for other cars. This trained path followed by the car along with the samples from the sensors attached to the car and the steering, braking and velocity during the period of reflex movement is stored in a matrix according to the samples taken at various time instances and accessed later when a similar instance is encountered.Figure 1. Lane changing algorithmIII.BUMPER TO BUMPER TRAFFIC DRIVING AND FREEWAYCRUISE CONTROLDriving becomes relatively tedious when the car has to observe the speed, steering and braking exactly like the car that it is following. The skills required to drive are un-important in these situations [2]. Hence, we propose to track the car in front using light and ultrasound distance sensors and maintain a safe braking distance depending on the speed that our car adopts [15]. The equations for calculating the distance required to slow down from a given speed and acceleration are available from Newton’s laws of motion. This distance is constantly updated in the car’s system and with variation in speed, the distance is accordingly varied by changing the speed first and then attaining the required speed. This algorithm prevents unnecessary collisions, which are caused due to driver negligence when all the driver has to do is follow themovement of the car in front. We usually define two kindsof spaces existing around the car, personal space and regionof interest (ROI) [9]. The sensors that are fitted on the cardetect any object that enters the ROI. This can be achievedby distance rangesensors on the body of the car or by usingultrasound sensors along with image processing toconstantly follow the moving object in front of it.Thepersonal space is a part of the ROI. If there are no obstacleswithin the ROI, the vehicle follows the path leading to thedestination. But, if an obstacle enters into the personalspace, a virtual forcestarts to control the vehicle. The forceis adjusted to consider all the obstacles in the ROI such thatit maneuvers the host safely away from the obstacle in itspath. This also requires the ability to sense the host’sposition relative to the road. This adaptive cruise controlcan also be used when the traffic is slow moving. It willautomatically propel the car for minute distances as thevehicle in front progresses, without any human command.The human estimation or ability to predict the next move ofthe vehicle in front is made moot by the car’s controller,which will constantly be updated with the speed andorientation of moving and static obstacles in thesurrounding and hence predict the car’s exact position anthe next instant of time and adapt its movement accordinglysuch that it can dodge it successfully.Figure 2. Dynamically changing map of the obstacles in front of the carobtained by the rotating LiDAR sensor setup on the car. The car is atposition zero and the obstacles lie at the depicted x and y coordinated fromthe car. The car chooses the path where the obstacles are the farthest andalso the width there is sufficient for the car to pass. In all 3 graphs, thereare 3 different positions that have the most gaps and the car chooses thatpath.IV.S ELF M APPINGVehicles today use GPS to get directions to get toa particular destination [11]. In case of any changes in theinfrastructure, which have not been updated in the mappingservice that a vehicle is using, it will not be able to figureout the new path, which is available. Also, in case of areaswhose mapping is not done, or when a car is supposed topark in a parking lot of any mall, etc. whose maps are notavailable, it becomes necessary for a vehicle to create itsown map of the surrounding for its reference. A LiDARsensor is fitted on top of the car and it is rotated 360degrees in order to obtain distances of all the static ormoving obstacles in its proximity at that instant. Thesepoints are then joined in order to obtain the polar plot of theradius and angle at which the corresponding obstacles existin the surrounding. The map values at every instant are stored in a dynamically updated matrix which updates the distance and angle at which a particular obstacle lies with reference to the car as the car moves in its given orientation. The localized position of the car is obtained as stated in part 2 of this paper and the mapping values obtained at the same instants of time are stored in another dynamic matrix which works synchronized with respect to the position matrix of the car and hence, the map values are updated at every sampled position of the car. For example, if a static object is at position45 degrees from the car at a distance of 4m, as the car progresses by 1m in the same orientation, that object now lies at 57.11 degrees and at a distance of 3.37m from the car. Thus, another map is formed and by comparing this map with the map obtained at the previous time instant, a separate map is continuously formed and the car’s system is made to use this for navigation. If an object doesn’t lie in its predicted location at the next time instant, it is assumed to be dynamic and hence, it is tracked accordingly, as there will be another location in the map where the predicted and obtained results will differ and that object would have reached there. This will help to find the dynamic object’s velocity as well as its orientation. This map will also assign directions to be followed by the car in case the destination is unknown as the direction in which the obstacles will be the farthest and that will have sufficient width for the car to pass will lead the path. In this way, the entire map of the area, which the vehicle traverses, is fed into its memory and a visual plot is also obtained for any references such as surveillance using a self-driving car.Figure 3.This is the map that is created by the LiDAR sensor mounted on the car for self-mapping. The polar plot of the surrounding is obtained and all the positions of obstacles around are stored in a dynamic matrix for this time instant. This graph is depicted for time t=0, and the position of the car is at (0,0). For reference, we take 5 points marked in red for the dynamic matrix evaluation. The position these points with respect to the car as it moves along the path specified in Fig. 5 is obtained in our dynamic matrix along with the speed of the car and its position at the sampling instant. This map further helps in creating the map of the environment with the help of past and future values of the dynamic matrix.V.M ULTI VEHICLE CO-OPERATIVE DRIVING AND JUNCTIONCOLLISION AVOIDANCE USING SWARM ROBOTICSIndividual-vehicle-control research focuses mainly on guaranteeing driving safety. Increased traffic congestion is making multivehicle-control research an important topic [8]. Using appropriate inter-vehicle communication to link vehicles, cooperative driving lets vehicles safely change lanes and merge into traffic, improving traffic control performance. We view each vehicle is an individual agent and determine the proper driving schedule through negotiation and planning. Then, virtual vehicle mapping and the trajectory planning methodsare used to handle the collision-free requirements and vehicle (dynamic and geometric) constraints are found out [16,12]. When a vehicle is crossing a junction, some spots are blind spots and just alerting the driver for a possible collision will not necessarily avert the collision, as the driver may not be able to use his reflexes [10]. Hence, it becomes necessary for the vehicles approaching at a junction to inter-communicate and change the speed or steer accordingly. As the reaction time is very less after the car detects a moving object approaching after the blind spot, just controlling the user’s car to avoid collision may not be fruitful. Instead if they form a pattern of alternate crossing after given instants of time depending on the individual speed and ability to brake or position to steer, collision is avoided.Safety is achieved in potential collision scenarios by controlling the velocities of both vehicles with automatic brake and throttle commands. Automatic commands can never cause the violation of predefined upper and lower speed limits [4]. The principle of swarm robotics also allows the cars to inter-communicate and form patterns to overcome paths of various types. If the cars suddenly overcome a bottleneck from a three-lane road to a single lane road, there will be a traffic jam at that instant. Instead, if the cars communicate internally and align one behind the other using the lane changing algorithms and the car in the front leads the entire swarm, the single lane patch can be passed without traffic or human intervention. Co-operative driving can hence be implemented if all cars are equipped with ITS [17]. The self-maps and the model predictive control will predict motion of the other cars.Figure 4.This figure shows the different formations that 4 vehicles in a swarm can form by inter-communicating and following the leader or a particular reference point depending the obstacle encountered [20].VI.CONCLUSIONSWe have successfully implemented vehiclelocalization with the help of dynamic matrix algorithms andalso created self-maps with the same. The model predictive control for anti-collision for an individual ITS equipped car has been tested under various circumstances. The virtual mapping and selecting the optimum path using Dijkstra’s algorithm and Kalman filters has also been implemented. Also, a low cost model of the LiDAR based sensor system used for self-mapping and collision-avoidance has been developed. The car is also successfully equipped with controls that can steer and maneuver it without human intervention to avoid obstacles depending on the inputs from the predicted controls with the help of dynamic matrices. This semi-auto pilot feature is still under research at MIT Research Labs for implementation in consumer cars [19].VII.FUTURE WORK AND SCOPEThe work on SWARM robotics for vehicle inter-communication for collision avoidance is still under work for full-scale implementation along with research on DSRC. 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Arkin. IEEE transactions on roboticsand automation vol. XX no. Y month 1999.Figure 5. This is the dynamically generated matrix in which a car is moved for 20m along the path specified by the coordinates of the car’s position at 11 sampling instants from time t=0 to t=1sec. The car’s speed and the position of obstacles marked in red in Fig. 3 at every sampling instant with respect to the car’s position at that instant is stored in the matrix. For reference, we have evaluated only 5 obstacles, but in actual scenario, all obstacles are evaluated in the same way in real time and this dynamic matrix is constantly updated with values.。