Download or read book Autonomous Vehicle Path Planning Using MPC and APF written by Zahra Elmi and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles have been at the forefront of academic and industrial research in recent decades. This study,Äôs aim is to reduce traffic congestion, improve safety, and accidents. Path planning algorithms are one of the main elements in autonomous vehicles that make critical decisions. Motion planning methods are required when transporting passengers from one point to another. These methods have incorporated several methods such as generating the best trajectory while considering the constraints of vehicle dynamics and obstacles, searching a path to follow, and avoiding obstacles that guarantee comfort, safety, and efficiency. We suggested an effective path planning algorithm based on Model Predictive Controller that determines the maneuvers mode such as lane-keeping and lane-changing automatically. We utilized two different artificial potential field functions for the road boundary, obstacles, and lane center to ensure safety. On the four scenarios, we examined the proposed path planning controller. The obtained results show that when a path planning controller is used, the vehicle avoids colliding with obstacles and follows the rules of the road by adjusting the vehicle,Äôs dynamics. An autonomous vehicle,Äôs safety is ensured by the path planning controller.

Download or read book Autonomous Road Vehicle Path Planning and Tracking Control written by Levent Guvenc and published by John Wiley & Sons. This book was released on 2021-12-06 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the latest research in path planning and robust path tracking control In Autonomous Road Vehicle Path Planning and Tracking Control, a team of distinguished researchers delivers a practical and insightful exploration of how to design robust path tracking control. The authors include easy to understand concepts that are immediately applicable to the work of practicing control engineers and graduate students working in autonomous driving applications. Controller parameters are presented graphically, and regions of guaranteed performance are simple to visualize and understand. The book discusses the limits of performance, as well as hardware-in-the-loop simulation and experimental results that are implementable in real-time. Concepts of collision and avoidance are explained within the same framework and a strong focus on the robustness of the introduced tracking controllers is maintained throughout. In addition to a continuous treatment of complex planning and control in one relevant application, the Autonomous Road Vehicle Path Planning and Tracking Control includes: A thorough introduction to path planning and robust path tracking control for autonomous road vehicles, as well as a literature review with key papers and recent developments in the area Comprehensive explorations of vehicle, path, and path tracking models, model-in-the-loop simulation models, and hardware-in-the-loop models Practical discussions of path generation and path modeling available in current literature In-depth examinations of collision free path planning and collision avoidance Perfect for advanced undergraduate and graduate students with an interest in autonomous vehicles, Autonomous Road Vehicle Path Planning and Tracking Control is also an indispensable reference for practicing engineers working in autonomous driving technologies and the mobility groups and sections of automotive OEMs.

Download or read book Path Planning for Autonomous Vehicle written by Umar Zakir Abdul Hamid and published by BoD – Books on Demand. This book was released on 2019-10-02 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Path Planning (PP) is one of the prerequisites in ensuring safe navigation and manoeuvrability control for driverless vehicles. Due to the dynamic nature of the real world, PP needs to address changing environments and how autonomous vehicles respond to them. This book explores PP in the context of road vehicles, robots, off-road scenarios, multi-robot motion, and unmanned aerial vehicles (UAVs ).

Download or read book MPC BASED AUTONOMOUS DRIVING CONTROL WITH LOCALIZED PATH PLANNING FOR OBSTACLE AVOIDANCE AND NAVIGATING SIGNALIZED INTERSECTIONS written by and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : Connected and autonomous vehicles are becoming the major focus of research for the industry and academia in the automotive field. Many companies and research groups have demonstrated the advantages and the requirement of such technology to improve the energy efficiency of vehicles, decrease the number of crash and road accidents, and control emissions. This research delves into improving the autonomy of self-driving vehicles by implementing localized path planning algorithms to introduce motion control for obstacle avoidance during uncertainties. Lateral path planning is implemented using the A* algorithm combined with piecewise Bezier curve generation which provides an optimum trajectory reference to avoid a collision. Model Predictive Control (MPC) is used to implement longitudinal and lateral control of the vehicle. The data from vehicle-to-everything (V2X) communication infrastructure is used to navigate through multiple signalized intersections. Furthermore, a new method of developing Advanced Driver Assistance Systems (ADAS) algorithms and vehicle controllers using Model-In-the-Loop (MIL) testing is explored with the use of PreScan®. With PreScan®, various traffic scenarios are modeled and the sensor data are simulated by using physics-based sensor models, which are fed to the controller for data processing and motion planning. Obstacle detection and collision avoidance are demonstrated using the presented MPC controller. The results of the proposed controller and the scope of the future work conclude the research.

Download or read book DEVELOPMENT OF AUTONOMOUS VEHICLE MOTION PLANNING AND CONTROL ALGORITHM WITH D PLANNER AND MODEL PREDICTIVE CONTROL IN A DYNAMIC ENVIRONMENT written by and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : The research in this report incorporates the improvement in the autonomous driving capability of self-driving cars in a dynamic environment. Global and local path planning are implemented using the D* path planning algorithm with a combined Cubic B-Spline trajectory generator, which generates an optimal obstacle free trajectory for the vehicle to follow and avoid collision. Model Predictive Control (MPC) is used for the longitudinal and the lateral control of the vehicle. The presented motion planning and control algorithm is tested using Model-In-the-Loop (MIL) method with the help of MATLAB® Driving Scenario Designer and Unreal Engine® Simulator by Epic Games®. Different traffic scenarios are built, and a camera sensor is configured to simulate the sensory data and feed it to the controller for further processing and vehicle motion planning. Simulation results of vehicle motion control with global and local path planning for dynamic obstacle avoidance are presented. The simulation results show that an autonomous vehicle follows a commanded velocity when the relative distance between the ego vehicle and an obstacle is greater than a calculated safe distance. When the relative distance is close to the safe distance, the ego vehicle maintains the headway. When an obstacle is detected by the ego vehicle and the ego vehicle wants to pass the obstacle, the ego vehicle performs obstacle avoidance maneuver by tracking desired lateral positions.

Download or read book Trajectory Planning of an Autonomous Vehicle in Multi Vehicle Traffic Scenarios written by Mahdi Morsali and published by Linköping University Electronic Press. This book was released on 2021-03-25 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tremendous industrial and academic progress and investments have been made in au-tonomous driving, but still many aspects are unknown and require further investigation,development and testing. A key part of an autonomous driving system is an efficient plan-ning algorithm with potential to reduce accidents, or even unpleasant and stressful drivingexperience. A higher degree of automated planning also makes it possible to have a betterenergy management strategy with improved performance through analysis of surroundingenvironment of autonomous vehicles and taking action in a timely manner. This thesis deals with planning of autonomous vehicles in different urban scenarios, road,and vehicle conditions. The main concerns in designing the planning algorithms, are realtime capability, safety and comfort. The planning algorithms developed in this thesis aretested in simulation traffic situations with multiple moving vehicles as obstacles. The re-search conducted in this thesis falls mainly into two parts, the first part investigates decou-pled trajectory planning algorithms with a focus on speed planning, and the second sectionexplores different coupled planning algorithms in spatiotemporal environments where pathand speed are calculated simultaneously. Additionally, a behavioral analysis is carried outto evaluate different tactical maneuvers the autonomous vehicle can have considering theinitial states of the ego and surrounding vehicles. Particularly relevant for heavy duty vehicles, the issues addressed in designing a safe speedplanner in the first part are road conditions such as banking, friction, road curvature andvehicle characteristics. The vehicle constraints on acceleration, jerk, steering, steer ratelimitations and other safety limitations such as rollover are further considerations in speedplanning algorithms. For real time purposes, a minimum working roll model is identified us-ing roll angle and lateral acceleration data collected in a heavy duty truck. In the decoupledplanners, collision avoiding is treated using a search and optimization based planner. In an autonomous vehicle, the structure of the road network is known to the vehicle throughmapping applications. Therefore, this key property can be used in planning algorithms toincrease efficiency. The second part of the thesis, is focused on handling moving obstaclesin a spatiotemporal environment and collision-free planning in complex urban structures.Spatiotemporal planning holds the benefits of exhaustive search and has advantages com-pared to decoupled planning, but the search space in spatiotemporal planning is complex.Support vector machine is used to simplify the search problem to make it more efficient.A SVM classifies the surrounding obstacles into two categories and efficiently calculate anobstacle free region for the ego vehicle. The formulation achieved by solving SVM, con-tains information about the initial point, destination, stationary and moving obstacles.These features, combined with smoothness property of the Gaussian kernel used in SVMformulation is proven to be able to solve complex planning missions in a safe way. Here, three algorithms are developed by taking advantages of SVM formulation, a greedysearch algorithm, an A* lattice based planner and a geometrical based planner. One general property used in all three algorithms is reduced search space through using SVM. In A*lattice based planner, significant improvement in calculation time, is achieved by using theinformation from SVM formulation to calculate a heuristic for planning. Using this heuristic,the planning algorithm treats a simple driving scenario and a complex urban structureequal, as the structure of the road network is included in SVM solution. Inspired byobserving significant improvements in calculation time using SVM heuristic and combiningthe collision information from SVM surfaces and smoothness property, a geometrical planneris proposed that leads to further improvements in calculation time. Realistic driving scenarios such as roundabouts, intersections and takeover maneuvers areused, to test the performance of the proposed algorithms in simulation. Different roadconditions with large banking, low friction and high curvature, and vehicles prone to safetyissues, specially rollover, are evaluated to calculate the speed profile limits. The trajectoriesachieved by the proposed algorithms are compared to profiles calculated by optimal controlsolutions.

Download or read book Belief State Planning for Autonomous Driving Planning with Interaction Uncertain Prediction and Uncertain Perception written by Hubmann, Constantin and published by KIT Scientific Publishing. This book was released on 2021-09-13 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive behavior of the other agents explicitly. Simulating the most likely future scenarios allows to find an optimal policy online that enables non-conservative planning under uncertainty.

Download or read book Comparison of Path Planning Approaches of Autonomous Vehicles for Obstacle Avoidance Application written by and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : This report investigates path planning and trajectory generation algorithms for the application in autonomous vehicles for obstacle avoidance. A literature review is conducted to select path planning and trajectory generation algorithms that are suitable for the obstacle avoidance application. Two path planning approaches are designed in this work. Approach 1 (RRT*-Spline) uses rapidly exploring random trees* (RRT*) path planning algorithm combined with cubic spline trajectory generation algorithm. Approach 2 (A*-Polynomial Curve) plans a feasible path by using A* algorithm and generates a smooth trajectory using 5th order polynomial curve fitting algorithm. To demonstrate obstacle avoidance of autonomous vehicles, Prescan and Matlab/Simulink are used to build an integrated model consisting of path planning and trajectory generation algorithms, an ego vehicle model, a Model Predictive Control (MPC) controller to control the longitudinal and lateral motion of the ego vehicle, and obstacle avoidance scenarios. Simulation tests are performed to validate the developed algorithms and compare the performances of two approaches for both stationary and moving obstacles. A summary of the performance comparison is provided with respect to the smoothness of the reference path, the smoothness of the reference trajectory, and the ego vehicle steering angle change while it performs an obstacle avoidance task.

Download or read book Motion Planning for Autonomous Vehicles in Partially Observable Environments written by Taş, Ömer Şahin and published by KIT Scientific Publishing. This book was released on 2023-10-23 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work develops a motion planner that compensates the deficiencies from perception modules by exploiting the reaction capabilities of a vehicle. The work analyzes present uncertainties and defines driving objectives together with constraints that ensure safety. The resulting problem is solved in real-time, in two distinct ways: first, with nonlinear optimization, and secondly, by framing it as a partially observable Markov decision process and approximating the solution with sampling.

Download or read book Path Planning and Tracking for Vehicle Collision Avoidance in Lateral and Longitudinal Motion Directions written by Jie Ji and published by Springer Nature. This book was released on 2022-06-01 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, the control of Connected and Automated Vehicles (CAVs) has attracted strong attention for various automotive applications. One of the important features demanded of CAVs is collision avoidance, whether it is a stationary or a moving obstacle. Due to complex traffic conditions and various vehicle dynamics, the collision avoidance system should ensure that the vehicle can avoid collision with other vehicles or obstacles in longitudinal and lateral directions simultaneously. The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via Forward Collision Warning (FCW), Brake Assist System (BAS), and Autonomous Emergency Braking (AEB), which has been commercially applied in many new vehicles launched by automobile enterprises. But in lateral motion direction, it is necessary to determine a flexible collision avoidance path in real time in case of detecting any obstacle. Then, a path-tracking algorithm is designed to assure that the vehicle will follow the predetermined path precisely, while guaranteeing certain comfort and vehicle stability over a wide range of velocities. In recent years, the rapid development of sensor, control, and communication technology has brought both possibilities and challenges to the improvement of vehicle collision avoidance capability, so collision avoidance system still needs to be further studied based on the emerging technologies. In this book, we provide a comprehensive overview of the current collision avoidance strategies for traditional vehicles and CAVs. First, the book introduces some emergency path planning methods that can be applied in global route design and local path generation situations which are the most common scenarios in driving. A comparison is made in the path-planning problem in both timing and performance between the conventional algorithms and emergency methods. In addition, this book introduces and designs an up-to-date path-planning method based on artificial potential field methods for collision avoidance, and verifies the effectiveness of this method in complex road environment. Next, in order to accurately track the predetermined path for collision avoidance, traditional control methods, humanlike control strategies, and intelligent approaches are discussed to solve the path-tracking problem and ensure the vehicle successfully avoids the collisions. In addition, this book designs and applies robust control to solve the path-tracking problem and verify its tracking effect in different scenarios. Finally, this book introduces the basic principles and test methods of AEB system for collision avoidance of a single vehicle. Meanwhile, by taking advantage of data sharing between vehicles based on V2X (vehicle-to-vehicle or vehicle-to-infrastructure) communication, pile-up accidents in longitudinal direction are effectively avoided through cooperative motion control of multiple vehicles.

Download or read book Path Planning and Robust Control of Autonomous Vehicles written by Sheng Zhu (Mechanical engineer) and published by . This book was released on 2020 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous driving is gaining popularity in research interest and industry investment over the last decade, due to its potential to increase driving safety to avoid driver errors which account for over 90% of all motor vehicle crashes. It could also help to improve public mobility especially for the disabled, and to boost the productivity due to enlarged traffic capacity and accelerated traffic flows. The path planning and following control, as the two essential modules for autonomous driving, still face critical challenges in implementations in a dynamically changing driving environment. For the local path/trajectory planning, multifold requirements need to be satisfied including reactivity to avoid collision with other objects, smooth curvature variation for passenger comfort, feasibility in terms of vehicle control, and the computation efficiency for real-time implementations. The feedback control is required afterward to accurately follow the planned path or trajectory by deciding appropriate actuator inputs, and favors smooth control variations to avoid sudden jerks. The control may also subject to instability or performance deterioration due to continuously changing operating conditions along with the model uncertainties. The dissertation contributes by raising the framework of path planning and control to address these challenges. Local on-road path planning methods from two-dimensional (2D) geometric path to the model-based state trajectory is explored. The latter one is emphasized due to its advantages in considering the vehicle model, state and control constraints to ensure dynamic feasibility. The real-time simulation is made possible with the adoption of control parameterization and lookup tables to reduce computation cost, with scenarios showing its smooth planning and the reactivity in collision avoidance with other traffic agents. The dissertation also explores both robust gain-scheduling law and model predictive control (MPC) for path following. The parameter-space approach is introduced in the former with validated robust performance under the uncertainty of vehicle load, speed and tire saturation parameter through hardware-in-the-loop and vehicle experiments. The focus is also put on improving the safety of the intended functionality (SOTIF) to account for the potential risks caused by lack of situational awareness in the absence of a system failure. Such safety hazards include the functional inability to comprehend the situation and the insufficient robustness to diverse conditions. The dissertation enhanced the SOTIF with parameter estimation through sensor fusion to increase the vehicle situational awareness of its internal and external conditions, such as the road friction coefficient. The estimated road friction coefficient helps in planning a dynamically feasible trajectory under adverse road condition. The integration of vehicle stability control with autonomous driving functions is also explored in the case that the road friction coefficient estimation is not responsive due to insufficiency in time and excitations.

Download or read book Motion Planning written by Edgar A. Martínez García and published by BoD – Books on Demand. This book was released on 2022-01-26 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms.

Download or read book Prioritized Obstacle Avoidance in Motion Planning of Autonomous Vehicles written by Yadollah Rasekhipour and published by . This book was released on 2017 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Driver errors are a critical factor of the majority of car crashes. Autonomous vehicles take drivers and driver errors out of the equation, so they are being developed to reduce car crashes. However, in some situations, a crash is unavoidable even for an autonomous vehicle. An autonomous vehicle is expected to behave properly in such a situation. Crashing into different obstacles have different costs based on the injury or damage the crash might cause. In an imminent crash situation, an autonomous vehicle is expected to consider these costs and plan a trajectory that avoids the obstacles with the highest priorities. In this thesis, a motion planning Model Predictive Controller (MPC) has been developed that plans the vehicle's trajectories based on the obstacle's priorities. Motion planning MPCs usually use potential fields or obstacle constraints for obstacle avoidance. However, they treat all the obstacles in the same way. Two methods have been developed in this thesis to prioritize obstacles in motion planning. The first method prioritizes obstacles based on their avoidance necessities. It categorizes obstacles as crossable and non-crossable, and assigns a potential function to each category corresponding to its avoidance necessity. The second method prioritizes obstacles based on their corresponding crash costs. It applies lexicographic optimization on the MPC to prioritize the non-crossable obstacles according to their crash costs by prioritizing their corresponding constraints. A motion planning MPC problem is generally a nonlinear MPC problem. It is usually approximated by a quadratic MPC problem to become implementable in real time. In this thesis, a quadratic motion planning MPC has been developed. This MPC has a linear vehicle model and linear vehicle and obstacle constraints. The linear vehicle model along with the linear vehicle constraints should be able to model the nonlinear vehicle behavior. A linear bicycle model has been utilized, and linear tire constraints have been developed such that they can model the nonlinear vehicle behavior at the tire force limits. Moreover, a linear obstacle constraint set misses some of the feasible trajectories in the process of convexifying the obstacle-free area. An iterative obstacle avoidance method has been developed in this thesis to reduce the number of feasible trajectories missed due to the convexification. The performance of the developed motion planning MPC has been evaluated in a computer simulation with a high fidelity vehicle model. The MPC has been simulated for test scenarios to evaluate its performance in autonomous driving and prioritizing obstacles. The capabilities of the developed tire constraints and the iterative obstacle avoidance method have also been observed. The motion planning MPC has also been implemented on an autonomous test vehicle platform to show that it is implementable in real time and to validate the simulation results.

Download or read book Real Time Collision Detection written by Christer Ericson and published by CRC Press. This book was released on 2004-12-22 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by an expert in the game industry, Christer Ericson's new book is a comprehensive guide to the components of efficient real-time collision detection systems. The book provides the tools and know-how needed to implement industrial-strength collision detection for the highly detailed dynamic environments of applications such as 3D games, virt

Download or read book Path Planning for Vehicles Operating in Uncertain 2D Environments written by Viacheslav Pshikhopov and published by Butterworth-Heinemann. This book was released on 2017-01-28 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Path Planning for Vehicles Operating in Uncertain 2D-environments presents a survey that includes several path planning methods developed using fuzzy logic, grapho-analytical search, neural networks, and neural-like structures, procedures of genetic search, and unstable motion modes. Presents a survey of accounting limitations imposed by vehicle dynamics Proposes modified and new original methods, including neural networking, grapho-analytical, and nature-inspired Gives tools for a novice researcher to select a method that would suit their needs or help to synthesize new hybrid methods

Download or read book Path Planning for Autonomous Vehicles Ensuring Reliable Driverless Navigation and Control Maneuver written by Muhammad Aizzat Zakaria and published by . This book was released on 2019 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Path Planning (PP) is one of the prerequisites in ensuring safe navigation and manoeuvrability control for driverless vehicles. Due to the dynamic nature of the real world, PP needs to address changing environments and how autonomous vehicles respond to them. This book explores PP in the context of road vehicles, robots, off-road scenarios, multi-robot motion, and unmanned aerial vehicles (UAVs ).

Download or read book Path Planning and Optimization of a High speed 1 5 Scale Off Road Autonomous Vehicle written by Andres Esparragoza and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates path-planning for a high-speed off-road autonomous vehicle. A 1/5th scale Remote Control (RC) vehicle was modified with sensors to be able to track vehicle position, trajectory, velocity, and other parameters necessary while performing traversals on a specifically designed off-road course that challenges the vehicles maneuverability and velocity. Advanced global position system (GPS) and encoder algorithms are implemented into the vehicles onboard microcontrollers to determine real time data necessary to dictate the necessary steering and throttle inputs. The sensor algorithms allow for the vehicle to measure its position and velocity, thereby enabling the foundation of path-following. Using algorithms to control the two receiving functions, steering and throttle, the vehicle must traverse the course autonomously with the aim of increasing speed per traversal until reaching an optimal steady-state velocity profile. Using iterative learning control (ILC) and similar control strategies, the vehicle learns from prior traversals to find limiting levels of velocity to be able to repeatedly produce traversals faster with each lap. The foundation for a 1/5th scale high-speed off-road vehicle path following simulation is created on MATLAB prior to vehicle implementation. The simulation successfully models constant speed along a path for both large-scale and 1/5th scale vehicles. The simulation is prepared for the implementation of ILC to determine the optimal steady-state velocity profile. The goal is for the simulation to achieve lap navigation faster than if a human were to control the vehicle via its remote-control unit. This research discussion concludes by outlining the next steps for algorithm development and field testing. The overall goal is to contribute to the development of autonomous off-road vehicles for use in a range of applications, including agriculture, mining, and search and rescue. The findings of this thesis seek to advance core algorithms and deployment technologies for autonomous systems in challenging off-road environments.