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Author: Ismail Bensekrane Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In this thesis, an energy planning for over-actuated unmanned road vehicles (URVs) with redundant steering configuration is proposed. In fact, indicators on the road geometry, the redundancy of actuation, the optimal velocity profile and the driving mode are identified for each segment of the URV's trajectory. Thus, a power consumption estimation model of an over-actuated autonomous driving vehicle is developed. Two methods for power consumption modeling are considered. The first method is based on an analytic model of power consumption, taking into account the degree of steerability, degree of mobility and degree of actuation redundancy. The second method used for power consumption modeling based on data-learning qualitative method namely: Adaptive Neuro Fuzzy Inference System (ANFIS). The latter has been considered in case of the presence of unknown dynamic parameters of the URV and uncertainties about its interaction with the environment. Validation of the estimation of the power consumption has been applied of real autonomous vehicle called RobuCar. Energy planning strategy has been built using two approaches, discrete and continuous. The discrete approach depends on a construction of an energy digraph with all feasible configurations taking into account kinematic and dynamic constraints based on a 3D grid map setup, according to: velocity, arc-length, driving mode. In this weighted directed graph, the edges describe the consumed energy by the UAV along a segment of a trajectory. An optimization algorithm is applied on the digraph to get a global optimal solution combining driving mode, power consumption and velocity profile of the URV. The continuous approach is based on a multi-criteria optimization strategy using genetic algorithms (NSGA-II). Then a real road path is considered and modeled by using two smooth geometrical combinations: the first one is {lines, clothoids and arcs}, and the second one is {lines and Pythagorean Hodograph (PH) curves}. The energy planning strategy is then applied to the generated paths. Also, a directed graph is built to synthesis the optimal velocity profile that minimizes the overall energy consumption while accounting for all driving modes. Results are compared with those given by the dynamic programming method for global offline optimization.
Author: Ismail Bensekrane Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In this thesis, an energy planning for over-actuated unmanned road vehicles (URVs) with redundant steering configuration is proposed. In fact, indicators on the road geometry, the redundancy of actuation, the optimal velocity profile and the driving mode are identified for each segment of the URV's trajectory. Thus, a power consumption estimation model of an over-actuated autonomous driving vehicle is developed. Two methods for power consumption modeling are considered. The first method is based on an analytic model of power consumption, taking into account the degree of steerability, degree of mobility and degree of actuation redundancy. The second method used for power consumption modeling based on data-learning qualitative method namely: Adaptive Neuro Fuzzy Inference System (ANFIS). The latter has been considered in case of the presence of unknown dynamic parameters of the URV and uncertainties about its interaction with the environment. Validation of the estimation of the power consumption has been applied of real autonomous vehicle called RobuCar. Energy planning strategy has been built using two approaches, discrete and continuous. The discrete approach depends on a construction of an energy digraph with all feasible configurations taking into account kinematic and dynamic constraints based on a 3D grid map setup, according to: velocity, arc-length, driving mode. In this weighted directed graph, the edges describe the consumed energy by the UAV along a segment of a trajectory. An optimization algorithm is applied on the digraph to get a global optimal solution combining driving mode, power consumption and velocity profile of the URV. The continuous approach is based on a multi-criteria optimization strategy using genetic algorithms (NSGA-II). Then a real road path is considered and modeled by using two smooth geometrical combinations: the first one is {lines, clothoids and arcs}, and the second one is {lines and Pythagorean Hodograph (PH) curves}. The energy planning strategy is then applied to the generated paths. Also, a directed graph is built to synthesis the optimal velocity profile that minimizes the overall energy consumption while accounting for all driving modes. Results are compared with those given by the dynamic programming method for global offline optimization.
Author: Levent Guvenc Publisher: John Wiley & Sons ISBN: 1119747961 Category : Technology & Engineering Languages : en Pages : 256
Book Description
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.
Author: Umar Zakir Abdul Hamid Publisher: BoD – Books on Demand ISBN: 1789239915 Category : Transportation Languages : en Pages : 150
Book Description
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 ).
Author: Steffen Heinrich Publisher: ISBN: 9783658219550 Category : Automated vehicles Languages : en Pages : 133
Book Description
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. Contents A Framework for Universal Driving Strategy Planning Sampling-Based Planning in Phase Space A Universal Approach for Driving Strategies Modeling Ego Motion Uncertainty Target Groups Scientists and students in the field of robotics, computer science, mechanical engineering Engineers in the field of vehicle automation, intelligent systems and robotics 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.
Author: Antonio Sciarretta Publisher: Springer ISBN: 3030241270 Category : Technology & Engineering Languages : en Pages : 294
Book Description
This book elaborates the science and engineering basis for energy-efficient driving in conventional and autonomous cars. After covering the physics of energy-efficient motion in conventional, hybrid, and electric powertrains, the book chiefly focuses on the energy-saving potential of connected and automated vehicles. It reveals how being connected to other vehicles and the infrastructure enables the anticipation of upcoming driving-relevant factors, e.g. hills, curves, slow traffic, state of traffic signals, and movements of nearby vehicles. In turn, automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and to save energy. Lastly, the energy-efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles. Building on classical methods of powertrain modeling, optimization, and optimal control, the book further develops the theory of energy-efficient driving. In addition, it presents numerous theoretical and applied case studies that highlight the real-world implications of the theory developed. The book is chiefly intended for undergraduate and graduate engineering students and industry practitioners with a background in mechanical, electrical, or automotive engineering, computer science or robotics.
Author: Mahdi Morsali Publisher: Linköping University Electronic Press ISBN: 9179296939 Category : Electronic books Languages : en Pages : 25
Book Description
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.
Author: Hubmann, Constantin Publisher: KIT Scientific Publishing ISBN: 3731510391 Category : Technology & Engineering Languages : en Pages : 178
Book Description
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.
Author: Sheng Zhu (Mechanical engineer) Publisher: ISBN: Category : Automated vehicles Languages : en Pages : 198
Book Description
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.
Author: James M. Anderson Publisher: Rand Corporation ISBN: 0833084372 Category : Transportation Languages : en Pages : 215
Book Description
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.
Author: Muhammad Aizzat Zakaria Publisher: ISBN: 9781839622854 Category : Motor vehicles. Aeronautics. Astronautics Languages : en Pages : 148
Book Description
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 ).
Author: Ismail Bensekrane
Author: Ismail Bensekrane
Author: Levent Guvenc
Author: Umar Zakir Abdul Hamid
Author: Steffen Heinrich
Author: Antonio Sciarretta
Author: Mahdi Morsali
Author: Hubmann, Constantin
Author: Sheng Zhu (Mechanical engineer)
Author: James M. Anderson
Author: Muhammad Aizzat Zakaria