Download or read book Vehicle Trajectory Prediction for Safe Navigation of Autonomous Vehicles written by Saptarishi Mukherjee and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Safe Trajectories and Sequential Bayesian Decision Making Architecture for Reliable Autonomous Vehicle Navigation written by Dimia Iberraken and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in Autonomous Vehicles (AV) driving raised up all the importance to ensure the complete reliability of AV maneuvers even in highly dynamic and uncertain environments/situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and complex configurations, AVs must have appropriate control architecture with reliable and real-time Risk Assessment and Management Strategies (RAMS). These targeted RAMS must lead to reduce drastically the navigation risks (theoretically, lower than any human-like driving behavior), with a systemic way. Consequently, the aim is also to reduce the need for too extensive testing (which could take several months and years for each produced RAMS without at the end having absolute prove). Hence the goal in this Ph.D. thesis is to have a provable methodology for AV RAMS. This dissertation addresses the full pipeline from risk assessment, path planning to decision-making and control of autonomous vehicles. In the first place, an overall Probabilistic Multi-Controller Architecture (P-MCA) is designed for safe autonomous driving under uncertainties. The P-MCA is composed of several interconnected modules that are responsible for: assessing the collision risk with all observed vehicles while considering their trajectories' predictions; planning the different driving maneuvers; making the decision on the most suitable actions to achieve; control the vehicle movement; aborting safely the engaged maneuver if necessary (due for instance to a sudden change in the environment); and as last resort planning evasive actions if there is no other choice. The proposed risk assessment is based on a dual-safety stage strategy. The first stage analyzes the actual driving situation and predicts potential collisions. This is performed while taking into consideration several dynamic constraints and traffic conditions that are known at the time of planning. The second stage is applied in real-time, during the maneuver achievement, where a safety verification mechanism is activated to quantify the risks and the criticality of the driving situation beyond the remaining time to achieve the maneuver. The decision-making strategy is based on a Sequential Decision Networks for Maneuver Selection and Verification (SDN-MSV) and corresponds to an important module of the P-MCA. This module is designed to manage several road maneuvers under uncertainties. It utilizes the defined safety stages assessment to propose discrete actions that allow to: derive appropriate maneuvers in a given traffic situation and provide a safety retrospection that updates in real-time the ego-vehicle movements according to the environment dynamic, in order to face any sudden hazardous and risky situation. In the latter case, it is proposed to compute the corresponding low-level control based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) that allows the ego-vehicle to pursue the advised collision-free evasive trajectory to avert an accident and to guarantee safety at any time.The reliability and the flexibility of the overall proposed P-MCA and its elementary components have been intensively validated, first in simulated traffic conditions, with various driving scenarios, and secondly, in real-time with the autonomous vehicles available at Institut Pascal.

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 Optimizing Safe Motion for Autonomous Vehicles written by Masahide Shirasaka and published by . This book was released on 1994 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are two goals for autonomous vehicle navigation planning: shortest path and safe path. These goals are often in conflict; path safety is more important. Safety of the autonomous vehicle's navigation is determined by the clearances between the vehicle and obstacles. Because a Voronoi boundary is the set of points locally maximizing the clearance from obstacles, safety is maximized on it. Therefore Voronoi Diagrams are suitable for motion planning of autonomous vehicles. We use the derivative of curvature k of the vehicle motion (dk/ds) as the only control variable for the vehicle where s is the length along the vehicle trajectory. Previous motion planning of the autonomous mobile robot Yamabico-11 at Naval Postgraduate School used a path tracking method. Before the mission began the vehicle was given a track to follow; motion planning consisted of calculating the point on the track closest to the vehicle and calculating dk/ ds then steering the vehicle to get onto track. We propose a method of planning safe motions of the vehicle to calculate optimal dk/ds at each point directly from the information of the world without calculating the track to follow. This safe navigation algorithm is fundamentally different from the path tracking using a path specification. Additionally motion planning is simpler and faster than the path tracking method. The effectiveness of this steering function for vehicle motion control is demonstrated by algorithmic simulation and by use on the autonomous mobile robot Yamabico 11 at the Naval Postgraduate School.

Download or read book Safe Intention aware Maneuvering of Autonomous Vehicles written by Xin Cyrus Huang and published by . This book was released on 2019 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to improve driving performance, while achieving safety in a dynamic environment, it is crucial for a vehicle motion planner to be aware of the intentions of the surrounding agent vehicles. Many existing approaches that ignore the intentions of surrounding vehicles would produce risky or over-conservative plans. In this thesis, we describe a maneuver motion planning system that achieves both safety and efficiency by estimating the types of surrounding drivers and the vehicle motions being executed by them over a finite horizon in the future. Our claim is that a vehicle is able to efficiently and safely navigate in dynamic traffic situations by estimating the possible types of drivers in its immediate vicinity, such as aggressive or careful, and by predicting their likely maneuvers and motions as probability distributions. To perform these predictions, we first employ a vehicle model that incorporates different driving styles, possible types of maneuvers, the likely trajectories that these maneuvers produce, and the likelihood of transitioning between successive maneuvers. The vehicle models are combined and encoded as hybrid partially observable Markov decision processes (POMDPs) whose discrete elements represent driving styles and maneuvers for each style and whose continuous parts represent vehicle motions. We then frame the problem of recognizing a vehicle's current driving style and maneuver as a belief state update on the hybrid POMDP. The driving style is assessed using multinomial logistic regression classification, while the maneuver is estimated using Bayesian filtering over a variant of probabilistic hybrid automata and a library of pre-learned motion primitive models. Multinomial logistic regression classification allows us to predict driving styles probabilistically using multiple driving features, and Bayesian filtering provides robust estimation results based on the prior information. Given the recognition results and the learned motion primitive models, we provide probabilistically sound predictions of the future maneuver and trajectory sequence of each agent vehicle. Finally, we compute safe motion plans of the ego vehicle in light of recognized agent vehicle driver styles, intended maneuvers, and future vehicle trajectories, by performing risk-bounded planning on the hybrid POMDP model. We demonstrate our system in a number of challenging simulated environments, including unprotected intersection left turns and lane changes with multiple dynamic vehicles. The demonstration shows that our intent recognition algorithms achieve an average driving style estimation accuracy of 89.89%, an average maneuver estimation accuracy of 98.9%, and an average trajectory prediction error of 2.12 meters. Furthermore, our maneuver planning system guarantees safety with respect to the safety constraint, while arriving at the goal 13.71% faster compared to a state-of-the-art planner without the intent recognition capability.

Download or read book Safe Navigation for Autonomous Vehicles in Dynamic Environments written by Luis Alfredo Martínez Gómez and published by . This book was released on 2010 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis deals with the problem of safe navigation for autonomous vehicles in dynamic environments. Motion safety is defined by means of Inevitable Collision States (ICS). An ICS is a state for which, no matter what the future trajectory of the vehicle is, a collision eventually occurs. For obvious safety reasons, an autonomous system should never ever find itself in one of such states. To accomplish this objective the problem is addressed in two parts. The first part focuses on determining which states are safe for the vehicle (non-ICS). The second part concentrates on how to select a valid control to move from one safe state to the other. Once it is found, the vehicle can apply it to successfully navigate the environment. Simulations and experimental results are presented to validate the approach.

Download or read book Safe and Robust Connected and Autonomous Vehicles in Mixed autonomy Traffic written by Rodolfo Valiente Romero and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous Vehicles (AVs) are expected to transform transportation in the near future. Although considerable progress has been made, widespread adoption of AVs will not become a reality until solutions are developed that enable AVs to co-exist with Human-driven Vehicles (HVs). There are still many challenges preventing Connected and Autonomous Vehicles (CAVs) from safely and smoothly navigating. We identify two major challenges in this direction. First, the communication system is not always reliable and suffers from noise and information loss. Second, AV navigation in the presence of HVs is challenging, as HVs continuously update their policies in response to AVs and the social preferences and behaviors of human drivers are unknown. Towards this end, we first propose solutions to improve situational awareness by enabling reliable and robust Cooperative Vehicle Safety (CVS) systems that mitigate the effect of information loss and propose a hybrid learning-based predictive modeling technique for CVS systems. Our prediction system is based on a Hybrid Gaussian Process (HGP) approach that provides accurate vehicle trajectory predictions to compensate for information loss. We use offline real-world data to learn a finite bank of driver models that represent the joint dynamics of the vehicle and the driver's behavior. AVs and HVs equipped with such reliable vehicular communication can coordinate, improving safety and efficiency. However, even in the presence of perfect communication, is still challenging for CAVs to navigate in the presence of humans. Therefore, we study the cooperative maneuver planning problem in a mixed autonomy environment. We frame the mixed-autonomy problem as a Multi-Agent Reinforcement Learning (MARL) problem and propose an approach that allows AVs to learn the decision-making of HVs implicitly from experience, account for all vehicles' interests, and safely adapt to other traffic situations. In contrast with existing works, we quantify AVs' social preferences and propose a distributed reward structure that introduces altruism into their decision-making process, allowing the altruistic AVs to learn to establish coalitions and influence the behavior of HVs. Inspired by humans, we provide our AVs with the capability of anticipating future states and leveraging prediction in the MARL decision-making framework. We propose the integration of two essential components of AVs, i.e, social navigation and prediction, and present a prediction-aware planning and social-aware optimization RL framework. Our proposed framework take advantage of a Hybrid Predictive Network (HPN) that anticipates future observations. The HPN is used in a multi-step prediction chain to compute a window of predicted future observations to be used by the Value Function Network (VFN). Finally, a safe VFN is trained to optimize a social utility using a sequence of previous and predicted observations, and a safety prioritizer is used to leverage the predictions to mask the unsafe actions, constraining the RL policy. The experiments on real-world and simulated data demonstrated the performance improvement of the proposed solutions in both safety and traffic-level metrics and validate the advantages and applicability of our solutions.

Download or read book Vehicle Maneuver Prediction Using Deep Learning Networks written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vehicle maneuver prediction plays an important role in ADAS (Advanced Driver Assistance Systems) and autonomous vehicles. It predicts the future behaviors of surrounding vehicles based on the current and past driving states of vehicles. Accurately predicting a vehicle's future trajectory and maneuver intentions is essential for safe and efficient navigation in traffic. Compared to conventional physics-based models, deep learning approaches are getting more popular due to their better performances in complicated real-world scenarios. This dissertation studies the temporal and spatial dependencies of vehicle maneuvers in a driving trip and investigate an innovative deep learning system to predict maneuvers of surrounding vehicles. Our method utilizes a combination of sensor data such as GPS, speed, acceleration, and videos to predict the future maneuver of a vehicle. The system contains LSTM (Long Short-Term Memory) or Transformer networks to learn information from past driving states, and graph neural networks to exploit the spatial relations between surrounding vehicles. We evaluate the proposed method on a large-scale real-world dataset and compare its performance with several state-of-the-art approaches. Our results show that our method significantly outperforms existing methods in terms of accuracy and robustness. In addition to the prediction performance, we also analyze the interpretability of the proposed method and demonstrate how it can be used to identify critical factors affecting maneuver prediction. This research provides a significant contribution to the field of vehicle maneuver prediction and lays the foundation for the development of advanced ADAS and autonomous driving systems. Our method has the potential to improve the safety and efficiency of road transportation and can be used to support the deployment of autonomous vehicles in complex driving scenarios.

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 Autonomous Vehicles for Safer Driving written by Ronald K Jurgen and published by SAE International. This book was released on 2013-04-16 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self-driving cars are no longer in the realm of science fiction, thanks to the integration of numerous automotive technologies that have matured over many years. Technologies such as adaptive cruise control, forward collision warning, lane departure warning, and V2V/V2I communications are being merged into one complex system. The papers in this compendium were carefully selected to bring the reader up to date on successful demonstrations of autonomous vehicles, ongoing projects, and what the future may hold for this technology. It is divided into three sections: overview, major design and test collaborations, and a sampling of autonomous vehicle research projects. The comprehensive overview paper covers the current state of autonomous vehicle research and development as well as obstacles to overcome and a possible roadmap for major new technology developments and collaborative relationships. The section on major design and test collaborations covers Sartre, DARPA contests, and the USDOT and the Crash Avoidance Metrics Partnership-Vehicle Safety Communications (CAMP-VSC2) Consortium. The final section presents seven SAE papers on significant recent and ongoing research by individual companies on a variety of approaches to autonomous vehicles. This book will be of interest to a wide range of readers: engineers at automakers and electronic component suppliers; software engineers; computer systems analysts and architects; academics and researchers within the electronics, computing, and automotive industries; legislators, managers, and other decision-makers in the government highway sector; traffic safety professionals; and insurance and legal practitioners.

Download or read book Robust Environmental Perception and Reliability Control for Intelligent Vehicles written by Huihui Pan and published by Springer Nature. This book was released on 2023-11-25 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.

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 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 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 Sensing and Control for Autonomous Vehicles written by Thor I. Fossen and published by Springer. This book was released on 2017-05-26 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume includes thoroughly collected on sensing and control for autonomous vehicles. Guidance, navigation and motion control systems for autonomous vehicles are increasingly important in land-based, marine and aerial operations. Autonomous underwater vehicles may be used for pipeline inspection, light intervention work, underwater survey and collection of oceanographic/biological data. Autonomous unmanned aerial systems can be used in a large number of applications such as inspection, monitoring, data collection, surveillance, etc. At present, vehicles operate with limited autonomy and a minimum of intelligence. There is a growing interest for cooperative and coordinated multi-vehicle systems, real-time re-planning, robust autonomous navigation systems and robust autonomous control of vehicles. Unmanned vehicles with high levels of autonomy may be used for safe and efficient collection of environmental data, for assimilation of climate and environmental models and to complement global satellite systems. The target audience primarily comprises research experts in the field of control theory, but the book may also be beneficial for graduate students.

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.