Driver Lane Change Intention Inference Using Machine Learning Methods PDF Download
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Author: Yang Xing
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Yang Xing
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Yang Xing
Publisher: Elsevier
ISBN: 0128191147
Category : Technology & Engineering
Languages : en
Pages : 260
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Book Description
Advanced Driver Intention Inference: Theory and Design describes one of the most important function for future ADAS, namely, the driver intention inference. The book contains the state-of-art knowledge on the construction of driver intention inference system, providing a better understanding on how the human driver intention mechanism will contribute to a more naturalistic on-board decision system for automated vehicles. Features examples of using machine learning/deep learning to build industry products Depicts future trends for driver behavior detection and driver intention inference Discuss traffic context perception techniques that predict driver intentions such as Lidar and GPS
Author: Hua Huang
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 0
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Book Description
In the foreseeable future, autonomous vehicles will have to drive alongside human drivers. In the absence of vehicle-to-vehicle communication, they will have to be able to predict the other road users' intentions. Equally importantly, they will also need to behave like a typical human driver such that other road users can infer their actions. It is critical to be able to learn a human driver's mental model and integrate it into the planning and control algorithm. In this dissertation, we first present a robust method to predict lane changes as cooperative or adversarial. For that, we first introduce a method to annotate lane changes as cooperative and adversarial based on the entire lane change trajectory. We then propose to train a specially designed neural network to predict the lane change label before the lane change has occurred and quantify the prediction uncertainty. The model will make lane change decisions following human drivers' driving habits and preferences, id est, it will only change lanes when the surrounding traffic is considered to be appropriate for the majority of human drivers. It will also recognize unseen novel samples and output low prediction confidence correspondingly, to alert the driver to take control or take conservative actions in such cases.
Author: HongSheng Qi
Publisher: Springer Nature
ISBN: 9819735971
Category :
Languages : en
Pages : 388
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Book Description
Author: Jorge Villagra
Publisher: Elsevier
ISBN: 0323985491
Category : Technology & Engineering
Languages : en
Pages : 426
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Book Description
Decision-Making Techniques for Autonomous Vehicles provides a general overview of control and decision-making tools that could be used in autonomous vehicles. Motion prediction and planning tools are presented, along with the use of machine learning and adaptability to improve performance of algorithms in real scenarios. The book then examines how driver monitoring and behavior analysis are used produce comprehensive and predictable reactions in automated vehicles. The book ultimately covers regulatory and ethical issues to consider for implementing correct and robust decision-making. This book is for researchers as well as Masters and PhD students working with autonomous vehicles and decision algorithms. Provides a complete overview of decision-making and control techniques for autonomous vehicles Includes technical, physical, and mathematical explanations to provide knowledge for implementation of tools Features machine learning to improve performance of decision-making algorithms Shows how regulations and ethics influence the development and implementation of these algorithms in real scenarios
Author: Katie Plant and Gesa Praetorius
Publisher: AHFE International
ISBN: 1958651362
Category : Technology & Engineering
Languages : en
Pages : 763
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Book Description
Human Factors in Transportation Proceedings of the 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022), July 24–28, 2022, New York, USA
Author: Anik Das
Publisher:
ISBN:
Category : Aggressiveness
Languages : en
Pages : 302
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Book Description
The presence of adverse weather has a significant negative impact on driving. This research investigated driver lateral behavior under adverse weather via Big Data analytics, Machine Learning, Data Mining in addition to traditional parametric modeling using trajectory-level SHRP2 Naturalistic Driving Study datasets. Initially, driver lane-keeping behavior in adverse weather was examined using ordered logistic regression approach, which indicated that environmental, traffic, driver, and roadway characteristics affect lane-keeping ability. The following study leveraged association rules mining that demonstrated a high association of affected visibility with poor lane-keeping performance. This research was then extended to investigate lane-changing characteristics, which revealed that conservative drivers had longer lane-changing durations in heavy fog compared to clear weather. Moreover, the research provided extensive evaluation into another lateral behavior, named lane-changing gap acceptance, using Multivariate Adaptive Regression Splines. The findings illustrated that relative speed between lane-changing and lead vehicle, acceleration of lane-changing and following vehicle, traffic conditions, and roadway geometries have effects on gap acceptance behavior. Subsequently, emphasis has been provided on developing reliable, accurate, and efficient Machine Learning-based lane change detection and prediction models through a data fusion approach considering different data availability. Finally, the research focused on developing weather-based microsimulation lane change models indicating that weather-specific lane changes were unique and hence, microsimulation models should be weather-specific. The outcomes of this research have significant implications, which could be used in microsimulation model calibration related to lateral behavior and safety improvements in Connected and Autonomous Vehicles, especially in adverse weather.
Author: Panos M. Pardalos
Publisher: Springer
ISBN: 3319514695
Category : Computers
Languages : en
Pages : 475
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Book Description
This book constitutes revised selected papers from the Second International Workshop on Machine Learning, Optimization, and Big Data, MOD 2016, held in Volterra, Italy, in August 2016. The 40 papers presented in this volume were carefully reviewed and selected from 97 submissions. These proceedings contain papers in the fields of Machine Learning, Computational Optimization and DataScience presenting a substantial array of ideas, technologies, algorithms, methods and applications.
Author: Johannes Edelmann
Publisher: CRC Press
ISBN: 1351966715
Category : Technology & Engineering
Languages : en
Pages : 726
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Book Description
The AVEC symposium is a leading international conference in the fields of vehicle dynamics and advanced vehicle control, bringing together scientists and engineers from academia and automotive industry. The first symposium was held in 1992 in Yokohama, Japan. Since then, biennial AVEC symposia have been established internationally and have considerably contributed to the progress of technology in automotive research and development. In 2016 the 13th International Symposium on Advanced Vehicle Control (AVEC’16) was held in Munich, Germany, from 13th to 16th of September 2016. The symposium was hosted by the Munich University of Applied Sciences. AVEC’16 puts a special focus on automatic driving, autonomous driving functions and driver assist systems, integrated control of interacting control systems, controlled suspension systems, active wheel torque distribution, and vehicle state and parameter estimation. 132 papers were presented at the symposium and are published in these proceedings as full paper contributions. The papers review the latest research developments and practical applications in highly relevant areas of vehicle control, and may serve as a reference for researchers and engineers.
Author: Hamido Fujita
Publisher: Springer Nature
ISBN: 3031085302
Category : Computers
Languages : en
Pages : 932
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Book Description
This book constitutes the thoroughly refereed proceedings of the 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, held in Kitakyushu, Japan, in July 2022. The 67 full papers and 11 short papers presented were carefully reviewed and selected from 127 submissions. The IEA/AIE 2022 conference focuses on focuses on applications of applied intelligent systems to solve real-life problems in all areas including business and finance, science, engineering, industry, cyberspace, bioinformatics, automation, robotics, medicine and biomedicine, and human-machine interactions.
