Download or read book Using Deep Learning to Predict Obstacle Trajectories for Collision Avoidance in Autonomous Vehicles written by Jaskaran Virdi and published by . This book was released on 2018 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a part of developing autonomous vehicles and better Advanced driver assistance systems (ADAS), it is important to consider how the spatio-temporal activities of other agents in the environment like pedestrians, vehicles, etc. which are competing for space on roads might impact the motion planning performance of the vehicle . A system which can predict future obstacle trajectories as well as warn the driver or the autonomous vehicle about an impending collision will lead to safer roads and save lives. Previous vehicle trajectory prediction approaches use motion models which have assumptions like constant velocity or constant acceleration which doesn't generalize well. Our approach is completely data driven and gives promising results for predicting trajectory of the obstacle up to 2 seconds in the future using a deep recurrent neural network. Taking inspiration from the recent success of sequence-to-sequence models in language translation we apply sequence-to-sequence recurrent neural networks to the new problem of trajectory prediction. The proposed scheme feeds the sequence of obstacles' past trajectory data obtained from sensors like LIDAR and GPS to the LSTM and predicts the position of the obstacle at future time steps. We use the KITTI dataset which provides us with annotated trajectory data for learning and evaluation.

Download or read book Deep Learning for Autonomous Vehicle Control written by Sampo Kuutti and published by Springer Nature. This book was released on 2022-06-01 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.

Download or read book Contingency Planning and Obstacle Anticipation for Autonomous Driving written by Jason Scott Hardy and published by . This book was released on 2013 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis explores the challenge of robustly handling dynamic obstacle uncertainty in autonomous driving systems. The path planning performance of Cornell's autonomous vehicle platform Skynet in the DARPA Urban Challenge (DUC) is analyzed and a new contingency planning formulation is presented that incorporates anticipated obstacle motions for improved collision avoidance capabilities. A discrete set of trajectory predictions is generated for each dynamic obstacle in the environment based on possible maneuvers the obstacle might make. A set of contingency paths is then optimized in real-time to accurately account for the mutually exclusive nature of these obstacle predictions. Computational scaling is addressed using a trajectory clustering algorithm that allows the contingency planner to plan a fixed number of paths regardless of the number of dynamic obstacles and possible obstacle goals in the environment. This contingency planning approach is evaluated using a series of human-inthe-loop experiments and simulations and is found to offer significant improvements in safety compared to the DUC planner and in performance compared to non-contingency planning approaches. A method for performing multi-step prediction over a two-stage Gaussian Process (GP) model is also presented. This prediction method is applied to a two-stage driver-vehicle obstacle model for the generation of high quality obstacle motion predictions using observed obstacle trajectories. An on-the-fly data selection technique is used to minimize computation when analytically evaluating higher order moments of the GP output. An adaptive Gaussian mixture model approach is also presented that allows this prediction technique to accurately predict the motion of highly nonlinear and multimodal systems.

Download or read book Safe Interactive Motion Planning for Autonomous Cars written by Mingyu Wang and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decade, the autonomous driving industry has seen tremendous advancements thanks to the progress in computation, artificial intelligence, sensing capabilities, and other technologies related to autonomous vehicles. Today, autonomous cars operate in dense urban traffic, compared to the last generation of robots that were confined to isolated workspaces. In these human-populated environments, autonomous cars need to understand their surroundings and behave in an interpretable, human-like manner. In addition, autonomous robots are engaged in more social interactions with other humans, which requires an understanding of how multiple reactive agents act. For example, during lane changes, most attentive drivers would slow down to give space if an adjacent car shows signs of executing a lane change. For an autonomous car, understanding the mutual dependence between its action and others' actions is essential for the safety and viability of the autonomous driving industry. However, most existing trajectory planning approaches ignore the coupling between all agents' behaviors and treat the decisions of other agents as immutable. As a result, the planned trajectories are conservative, less intuitive, and may lead to unsafe behaviors. To address these challenges, we present motion planning frameworks that maintain the coupling of prediction and planning by explicitly modeling their mutual dependency. In the first part, we examine reciprocal collision avoidance behaviors among a group of intelligent robots. We propose a distributed, real-time collision avoidance algorithm based on Voronoi diagrams that only requires relative position measurements from onboard sensors. When necessary, the proposed controller minimally modifies a nominal control input and provides collision avoidance behaviors even with noisy sensor measurements. In the second part, we introduce a nonlinear receding horizon game-theoretic planner that approximates a Nash equilibrium in competitive scenarios among multiple cars. The proposed planner uses a sensitivity-enhanced objective function and iteratively plans for the ego vehicle and the other vehicles to reach an equilibrium strategy. The resulting trajectories show that the ego vehicle can leverage its influence on other vehicles' decisions and intentionally change their courses. The resulting trajectories exhibit rich interactive behaviors, such as blocking and overtaking in competitive scenarios among multiple cars. In the last part, we propose a risk-aware game-theoretic planner that takes into account uncertainties of the future trajectories. We propose an iterative dynamic programming algorithm to solve a feedback equilibrium strategy set for interacting agents with different risk sensitivities. Through simulations, we show that risk-aware planners generate safer behaviors when facing uncertainties in safety-critical situations. We also present a solution for the "inverse" risk-sensitive planning algorithm. The goal of the inverse problem is to learn the cost function as well as risk sensitivity for each individual. The proposed algorithm learns the cost function parameters from datasets collected from demonstrations with various risk sensitivity. Using the learned cost function, the ego vehicle can estimate the risk profile of an interacting agent online to improve safety and efficiency.

Download or read book Predicting Vehicle Trajectory written by Cesar Barrios and published by CRC Press. This book was released on 2017-03-03 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book concentrates on improving the prediction of a vehicle’s future trajectory, particularly on non-straight paths. Having an accurate prediction of where a vehicle is heading is crucial for the system to reliably determine possible path intersections of more than one vehicle at the same time. The US DOT will be mandating that all vehicle manufacturers begin implementing V2V and V2I systems, so very soon collision avoidance systems will no longer rely on line of sight sensors, but instead will be able to take into account another vehicle’s spatial movements to determine if the future trajectories of the vehicles will intersect at the same time. Furthermore, the book introduces the reader to some improvements when predicting the future trajectory of a vehicle and presents a novel temporary solution on how to speed up the implementation of such V2V collision avoidance systems. Additionally, it evaluates whether smartphones can be used for trajectory predictions, in an attempt to populate a V2V collision avoidance system faster than a vehicle manufacturer can.

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 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 Creating Autonomous Vehicle Systems written by Shaoshan Liu and published by Morgan & Claypool Publishers. This book was released on 2017-10-25 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.

Download or read book Collision Avoidance Up to the Handling Limits for Autonomous Vehicles written by Joseph Funke and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: As autonomous vehicles enter public roads, they should be capable of using all of the vehicle's performance capability, if necessary, to avoid collisions. This dissertation focuses on facilitating collision avoidance for autonomous vehicles by enabling safe vehicle operation up to the handling limits. The new control approaches first rely on a standard paradigm for autonomous vehicles that divides vehicle control into trajectory generation and trajectory tracking. A trajectory generation approach calculates emergency lane change trajectories, defined in terms of path curvature, that allows an autonomous vehicle to perform emergency lane changes up to its handling limits. Analysis also provides insights into when and to what extent a vehicle should brake and turn during an emergency lane change to maximize the number of situations in which a collision can be avoided. However, experimental results also highlight vehicle stabilization challenges associated with tracking paths defined by high rates of curvature change, which are desirable for emergency maneuvers. A link is forged between path curvature and vehicle performance, which inspires two trajectory tracking control designs. A four-wheel steering controller adds rear steering actuation to improve tracking and stabilization performance, while a two-wheel steering predictive controller incorporates future path information into current control actions. Experimental results demonstrate the advantages of each approach. However, separating vehicle control into trajectory generation and tracking is not always conducive to emergency maneuvers up to the vehicle's handling limits, where these aspects of vehicle control become tightly coupled with each other and with vehicle stabilization. An alternative paradigm is suggested that is more adept at controlling the vehicle in such scenarios. This approach integrates trajectory generation, trajectory tracking, and vehicle stabilization into one controller capable of mediating among the sometimes conflicting demands imposed by collision avoidance and stabilization. The controller can prioritize collision avoidance, above even stabilization, to minimize potential collisions. Experimental emergency lane changes and a mid-corner obstacle avoidance scenario highlight the advantages of this integrated approach to vehicle control.

Download or read book Evaluation of Model Predictive Control Method for Collision Avoidance of Automated Vehicles written by Hikmet D. Ozdemir and published by . This book was released on 2020 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collision avoidance design plays an essential role in autonomous vehicle technology. It's an attractive research area that will need much experimentation in the future. This research area is very important for providing the maximum safety to automated vehicles, which have to be tested several times under diFFerent circumstances for safety before use in real life. This thesis proposes a method for designing and presenting a collision avoidance maneuver by using a model predictive controller with a moving obstacle for automated vehicles. It consists of a plant model, an adaptive MPC controller, and a reference trajectory. The proposed strategy applies a dynamic bicycle model as the plant model, adaptive model predictive controller for the lateral control, and a custom reference trajectory for the scenario design. The model was developed using the Model Predictive Control Toolbox and Automated Driving Toolbox in Matlab. Builtin tools available in Matlab/Simulink were used to verify the modeling approach and analyze the performance of the system. The major contribution of this thesis work was implementing a novel dynamic obstacle avoidance control method for automated vehicles. The study used validated parameters obtained from previous research. The novelty of this research was performing the studies using a MPC based controller instead of a sliding mode controller, that was primarily used in other studies. The results obtained from the study are compared with the validated models. The comparisons consisted of the lateral overlap, lateral error, and steering angle simulation results between the models. Additionally, this study also included outcomes for the yaw angle. The comparisons and other outcomes obtained in this study indicated that the developed control model produced reasonably acceptable results and recommendations for future studies.

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 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 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 Real time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain Changing Intentions written by Sarah Kathryn Ferguson and published by . This book was released on 2014 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: To plan safe trajectories in urban environments, autonomous vehicles must be able to interact safely and intelligently with other dynamic agents. Due to the inherent structure of these environments, drivers and pedestrians tend to exhibit a common set of motion patterns. The challenges are therefore to learn these motion patterns such that they can be used to predict future trajectories, and to plan safe paths that incorporate these predictions. This thesis considers the modeling and robust avoidance of pedestrians in real time. Pedestrians are particularly difficult to model, as their motion patterns are often uncertain and/or unknown a priori. The modeling approach incorporates uncertainty in both intent (i.e., where is the pedestrian going?) and trajectory associated with each intent (i.e., how will he/she get to this location?), both of which are necessary for robust collision avoidance. A novel changepoint detection and clustering algorithm (Changepoint-DPGP) is presented to enable quick detection of changes in pedestrian behavior and online learning of new behaviors not previously observed in prior training data. The resulting long-term movement predictions demonstrate improved accuracy in terms of both intent and trajectory prediction, relative to existing methods which consider only intent or trajectory. An additional contribution of this thesis is the integration of these predictions with a chance-constrained motion planner, such that trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware components and relevant control and data acquisition algorithms for an autonomous test vehicle are implemented and developed. Experiments demonstrate that an autonomous mobile robot utilizing this framework can accurately predict pedestrian motion patterns from onboard sensor/perception data and safely navigate within a dynamic environment

Download or read book Calculation of Collision Probability for Autonomous Vehicles Using Trajectory Prediction written by Gayatri Powar and published by . This book was released on 2016 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this thesis is to create a decision making algorithm. The goal would be to check the feasibility of the current maneuver by nding the probability of collision between the subject and target vehicles. These outputs can be used by other Advanced Driver Assistance System (ADAS) features including path planner, lateral control, longitudinal control, etc. We make use of some sensors like camera/radar (simulated data) and fuse these together for better estimation of measurements. With earlier experience with cameras, they are really poor at giving longitudinal distances whereas radars give more accurate measurements longitudinally. Using these measurements about targets ahead in the environment, we predict the trajectories of the obstacles/targets as well as the subject vehicle (autonomous vehicle). The algorithm predicts if it is safe to continue with the current maneuver in the near future for several seconds ahead of time, and makes the decision if the maneuver is possible. The results are obtained using probabilistic approach whether the future trajectories are going to collide. The thesis primarily focuses on target tracking, e cient sensor data fusion and future collision estimation. With the lessons learnt using existing literature an e cient approach is employed. The simulation is performed in PreScan simulator and MATLAB. Enhancements in the sensor data fusion using standby measurements and quasi-decentralized approach to combine measurements to yield improved results and achieve better scalability are proposed and implemented.

Download or read book Articulated Motion and Deformable Objects written by Francisco José Perales and published by Springer. This book was released on 2018-07-03 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Conference on Articulated Motion and Deformable Objects, AMDO 2018, held in Palma de Mallorca, Spain, in July 2018. The 12 papers presented were carefully reviewed and selected from 26 submissions. The papers address the following topics: advanced computer graphics and immersive videogames; human modeling and animation; human motion analysis and tracking; 3D human reconstruction and recognition; multimodal user interaction and applications; ubiquitous and social computing; design tools; input technology; programming user interfaces; 3D medical deformable models and visualization; deep learning methods for computer vision and graphics; and multibiometric.

Download or read book Algorithmic Foundations of Robotics X written by Emilio Frazzoli and published by Springer. This book was released on 2013-02-14 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms are a fundamental component of robotic systems. Robot algorithms process inputs from sensors that provide noisy and partial data, build geometric and physical models of the world, plan high-and low-level actions at different time horizons, and execute these actions on actuators with limited precision. The design and analysis of robot algorithms raise a unique combination of questions from many elds, including control theory, computational geometry and topology, geometrical and physical modeling, reasoning under uncertainty, probabilistic algorithms, game theory, and theoretical computer science. The Workshop on Algorithmic Foundations of Robotics (WAFR) is a single-track meeting of leading researchers in the eld of robot algorithms. Since its inception in 1994, WAFR has been held every other year, and has provided one of the premiere venues for the publication of some of the eld's most important and lasting contributions. This books contains the proceedings of the tenth WAFR, held on June 13{15 2012 at the Massachusetts Institute of Technology. The 37 papers included in this book cover a broad range of topics, from fundamental theoretical issues in robot motion planning, control, and perception, to novel applications.