Download or read book Safety and Efficiency in Autonomous Vehicles Through Planning with Uncertainty written by Zachary Nolan Sunberg and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Safety is the highest priority for autonomous vehicles, but if they are not also efficient in terms of time and other resources, they will have a significant competitive disadvantage and may not be adopted widely. Though safety and efficiency are opposing goals, better models and planning algorithms can result in simultaneous improvements to both. The partially observable Markov decision process (POMDP) provides a systematic framework for representing the chain of decisions that an autonomous vehicle makes when driving or flying. However, it is challenging to find optimal policies for POMDPs that represent continuous physical domains. This dissertation analyzes and demonstrates improvements related to several aspects of making safe and efficient decisions. First, it considers how pseudo-random approximate algorithms can be combined with trusted deterministic algorithms to make certification easier and increase reliability in an unmanned aerial vehicle domain. Second, simulation results demonstrate that modeling uncertainty in the internal states of other road users using POMDP planning can lead to significant improvement over a formulation that models only outcome uncertainty. Third, the research shows that current leading online POMDP algorithms are unable to solve some problems with continuous observation spaces and overcomes this weakness using double progressive widening and weighted particle filtering resulting in a new algorithm called POMCPOW. Finally, a description of the POMDPs.jl software framework is given.

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 Probabilistic Motion Planning for Automated Vehicles written by Naumann, Maximilian and published by KIT Scientific Publishing. This book was released on 2021-02-25 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: In motion planning for automated vehicles, a thorough uncertainty consideration is crucial to facilitate safe and convenient driving behavior. This work presents three motion planning approaches which are targeted towards the predominant uncertainties in different scenarios, along with an extended safety verification framework. The approaches consider uncertainties from imperfect perception, occlusions and limited sensor range, and also those in the behavior of other traffic participants.

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 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 Safe and Scalable Planning Under Uncertainty for Autonomous Driving written by Maxime Thomas Marcel Bouton and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous driving has the potential to significantly improve safety. Although progress has been made in recent years to deploy automated driving technologies, many situations handled on a daily basis by human drivers remain challenging for autonomous vehicles, such as navigating urban environments. They must reach their goal safely and efficiently while considering a multitude of traffic participants with rapidly changing behavior. Hand-engineering strategies to navigate such environments requires anticipating many possible situations and finding a suitable behavior for each, which places a large burden on the designer and is unlikely to scale to complicated situations. In addition, autonomous vehicles rely on on-board perception systems that give noisy estimates of the location and velocity of others on the road and are sensitive to occlusions. Autonomously navigating urban environments requires algorithms that reason about interactions with and between traffic participants with limited information. This thesis addresses the problem of automatically generating decision making strategies for autonomous vehicles in urban environments. Previous approaches relied on planning with respect to a mathematical model of the environment but have many limitations. A partially observable Markov decision process (POMDP) is a standard model for sequential decision making problems in dynamic, uncertain environments with imperfect sensor measurements. This thesis demonstrates a generic representation of driving scenarios as POMDPs, considering sensor occlusions and interactions between road users. A key contribution of this thesis is a methodology to scale POMDP approaches to complex environments involving a large number of traffic participants. To reduce the computational cost of considering multiple traffic participants, a decomposition method leveraging the strategies of interacting with a subset of road users is introduced. Decomposition methods can approximate the solutions to large sequential decision making problems at the expense of sacrificing optimality. This thesis introduces a new algorithm that uses deep reinforcement learning to bridge the gap with the optimal solution. Establishing trust in the generated decision strategies is also necessary for the deployment of autonomous vehicles. Methods to constrain a policy trained using reinforcement learning are introduced and combined with the proposed decomposition techniques. This method allows to learn policies with safety constraints. To address state uncertainty, a new methodology for computing probabilistic safety guarantees in partially observable domains is introduced. It is shown that the new method is more flexible and more scalable than previous work. The algorithmic contributions present in this thesis are applied to a variety of driving scenarios. Each algorithm is evaluated in simulation and compared to previous work. It is shown that the POMDP formulation in combination with scalable solving methods provide a flexible framework for planning under uncertainty for autonomous driving.

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 Uncertainty aware Spatiotemporal Perception for Autonomous Vehicles written by Mikhal Itkina and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles are set to revolutionize transportation in terms of safety and efficiency. However, autonomous systems still have challenges operating in complex human environments, such as an autonomous vehicle in a cluttered, dynamic urban setting. A key obstacle to deploying autonomous systems on the road is understanding, anticipating, and making inferences about human behaviors. Autonomous perception builds a general understanding of the environment for a robot. This includes making inferences about human behaviors in both space and time. Humans are difficult to model due to their vastly diverse behaviors and rapidly evolving objectives. Moreover, in cluttered settings, there are computational and visibility limitations. However, humans also possess desirable capabilities, such as their ability to generalize beyond their observed environment. Although learning-based systems have had success in recent years in modeling and imitating human behavior, efficiently capturing the data and model uncertainty for these systems remains an open problem. This thesis proposes algorithmic advances to uncertainty-aware autonomous perception systems in human environments. We make system-level contributions to spatiotemporal robot perception that reasons about human behavior, and foundational advancements in uncertainty-aware machine learning models for trajectory prediction. These contributions enable robotic systems to make uncertainty- and socially-aware spatiotemporal inferences about human behavior. Traditional robot perception is object-centric and modular, consisting of object detection, tracking, and trajectory prediction stages. These systems can fail prior to the prediction stage due to partial occlusions in the environment. We thus propose an alternative end-to-end paradigm for spatiotemporal environment prediction from a map-centric occupancy grid representation. Occupancy grids are robust to partial occlusions, can handle an arbitrary number of human agents in the scene, and do not require a priori information regarding the environment. We investigate the performance of computer vision techniques in this context and develop new mechanisms tailored to the task of spatiotemporal environment prediction. Spatially, robots also need to reason about fully occluded agents in their environment, which may occur due to sensor limitations or other agents on the road obstructing the field of view. Humans excel at extrapolating from their experiences by making inferences from observed social behaviors. We draw inspiration from human intuition to fill in portions of the robot's map that are not observable by traditional sensors. We infer occupancy in these occluded regions by learning a multimodal mapping from observed human driver behaviors to the environment ahead of them, thus treating people as sensors. Our system handles multiple observed agents to maximally inform the occupancy map around the robot. In order to safely integrate human behavior modeling into the robot autonomy stack, the perception system must efficiently account for uncertainty. Human behavior is often modeled using discrete latent spaces in learning-based models to capture the multimodality in the distribution. For example, in a trajectory prediction task, there may be multiple valid future predictions given a past trajectory. To accurately model this latent distribution, the latent space needs to be sufficiently large, leading to tractability concerns for downstream tasks, such as path planning. We address this issue by proposing a sparsification algorithm for discrete latent sample spaces that can be applied post hoc without sacrificing model performance. Our approach successfully balances multimodality and sparsity to achieve efficient data uncertainty estimation. Aside from modeling data uncertainty, learning-based autonomous systems must be aware of their model uncertainty or what they do not know. Flagging out-of-distribution or unknown scenarios encountered in the real world could be helpful to downstream autonomy stack components and to engineers for further system development. Although the machine learning community has been prolific in model uncertainty estimation for small benchmark problems, relatively little work has been done on estimating this uncertainty in complex, learning-based robotic systems. We propose efficiently learning the model uncertainty over an interpretable, low-dimensional latent space in the context of a trajectory prediction task. The algorithms presented in this thesis were validated on real-world autonomous driving data and baselined against state-of-the-art techniques. We show that drawing inspiration from human-level reasoning while modeling the associated uncertainty can inform environment understanding for autonomous perception systems. The contributions made in this thesis are a step towards uncertainty- and socially-aware autonomous systems that can function seamlessly in human environments.

Download or read book Autonomous Vehicle Technology written by James M. Anderson and published by Rand Corporation. This book was released on 2014-01-10 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.

Download or read book Measuring Automated Vehicle Safety written by Laura Fraade-Blanar and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report presents a framework for measuring safety in automated vehicles (AVs): how to define safety for AVs, how to measure safety for AVs, and how to communicate what is learned or understood about AVs.

Download or read book Planning and Simulation for Autonomous Vehicles in Urban Traffic Scenarios written by Xinchen Li (Ph. D. in electrical engineering) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic accidents result in a high number of fatalities each year. This brings up the importance of developing Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS), due to their potential of increasing traffic safety by reducing vehicle crashes caused by driver errors. It could also be helpful to deploy the intelligent transportation systems (ITS) in different traffic scenarios to increase the efficiency of traffic flow and enlarge the traffic capacity. Planning and control of the autonomous vehicles, the two essential modules in autonomous driving, are still facing severe challenges in adapting to various traffic scenarios and complex environments. The planning and decision making of vehicles in urban traffic environment are still a big challenge for autonomous vehicles due to its complexity and uncertainties. Hence it is necessary to develop decision making and planning algorithms for vehicles in urban traffic, especially in intersections. Also, velocity profile planning for autonomous vehicles is also required based on various requirements according to the environment. Additionally, a convenient method for testing and validating the developed algorithms is also required. Hence a good simulation environment is important in the field of autonomous vehicles. This dissertation contributes to planning and decision making of autonomous vehicles in urban traffic scenarios as well as developing a way of generating realistic simulation environments as test beds to validate developed autonomous driving algorithms. Decision making methods and planning methods for autonomous shuttles and autonomous vehicles in urban traffic are proposed. A rule based decision maker working for last mile problem is introduced for an autonomous shuttle so that the autonomous shuttle can deal with typical traffic on designated routes. Then to deal with complex and uncertain urban traffic scenarios when the ego autonomous vehicles doesn’t have full observability over other vehicles’ states, a Partially Observable Markov Decision Making Process (POMDP) based decision making algorithm is proposed for solving the roundabout intersection planning problem with multiple vehicles involved. Moreover, a velocity planning method for autonomous shuttle in geo-fenced area is developed, such that passengers in the autonomous shuttle are safe and comfortable. In order to improve the performance of decision making algorithms, vehicle behavior and trajectory prediction methods are also studied. Sensor perception is an important part of the autonomous driving as the ego autonomous vehicle is detecting the environment and surrounding vehicles all the time. Noise is inevitable during the perception and some internal states of other vehicles are not detected. Hence, a Kalman filter based vehicle trajectory tracking is introduced to take care the measurement noise in the perception as well as to estimate the vehicle internal states. A change point detection based policy prediction method is also introduced for determining the most likely vehicle behavior given a series of observation data along the vehicle trajectory. Combining both methods, a vehicle trajectory prediction over a future period of time is also proposed. In addition, a method for developing simulation environment using real map data and 3D rendering based on a game engine is presented as a powerful tool for developing simulations for intelligent transportation systems. All the proposed methods are provided with simulation and test results to demonstrate the efficiency.

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 Robust Motion Planning for Autonomous Tracked Vehicles in Deformable Terrain written by Sang Uk Lee (S.M.) and published by . This book was released on 2016 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ensuring the safety of autonomous vehicles during operation is a challenging task. Numerous factors such as process noise, sensor noise, incorrect model etc. can yield uncertainty in robot's state. Especially for tracked vehicles operating on rough terrain, vehicle slip due to vehicle terrain interaction affects the vehicle system significantly. In such cases, the motion planning of the autonomous vehicle must be performed robustly, considering the uncertain factors in advance of the real-time navigation. The primary contribution of this thesis is to present a robust optimal global planner for autonomous tracked vehicles operating in off-road terrain with uncertain slip. In order to achieve this goal, three tasks must be completed. First, the motion planner must be able to work efficiently under the non-holonomic vehicle system model. An approximate method is applied to the tracked vehicle system ensuring both optimality and efficiency. Second, the motion planner should ensure robustness. For this, a robust incremental sampling based motion planning algorithm (CC-RRT*) is combined with the LQG-MP algorithm. CC-RRT* yields the optimal and probabilistically feasible trajectory by using a chance constrained approach under the RRT* framework. LQG-MP provides the capability of considering the role of compensator in the motion planning phase and bounds the degree of uncertainty to appropriate size. Third, the effect of slip on the vehicle system must be modeled properly. This can be done in advance of operation if we have experimental data and full information about the environment. However, in case where such knowledge is not available, the online slip estimation can be performed using system identification method such as the IPEM algorithm. Simulation results shows that the resulting algorithms are efficient, optimal, and robust. The simulation was performed on a realistic scenario with several important factors that can increase the uncertainty of the vehicle. Experimental results are also provided to support the validity of the proposed algorithm. The proposed framework can be applied to other robotic systems where robustness is an important issue.

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 Autonomous Driving written by Markus Maurer and published by Springer. This book was released on 2016-05-21 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".

Download or read book Planning Universal On Road Driving Strategies for Automated Vehicles written by Steffen Heinrich and published by Springer. This book was released on 2018-04-19 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Steffen Heinrich describes a motion planning system for automated vehicles. The planning method is universally applicable to on-road scenarios and does not depend on a high-level maneuver selection automation for driving strategy guidance. The author presents a planning framework using graphics processing units (GPUs) for task parallelization. A method is introduced that solely uses a small set of rules and heuristics to generate driving strategies. It was possible to show that GPUs serve as an excellent enabler for real-time applications of trajectory planning methods. Like humans, computer-controlled vehicles have to be fully aware of their surroundings. Therefore, a contribution that maximizes scene knowledge through smart vehicle positioning is evaluated. A post-processing method for stochastic trajectory validation supports the search for longer-term trajectories which take ego-motion uncertainty into account. About the Author Steffen Heinrich has a strong background in robotics and artificial intelligence. Since 2009 he has been developing algorithms and software components for self-driving systems in research facilities and for automakers in Germany and the US.

Download or read book Shaping the Future of Autonomous Vehicles written by Nidhi Kalra and published by . This book was released on 2016 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Testimony presented before the Senate Appropriations Committee, Subcommittee on Transportation, Housing and Urban Development, and Related Agencies on November 16, 2016.