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Modeling Human Driving Behavior
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Book Synopsis Modelling Driver Behaviour in Automotive Environments by : Carlo Cacciabue
Download or read book Modelling Driver Behaviour in Automotive Environments written by Carlo Cacciabue and published by Springer Science & Business Media. This book was released on 2010-04-28 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a general overview of the various factors that contribute to modelling human behaviour in automotive environments. This long-awaited volume, written by world experts in the field, presents state-of-the-art research and case studies. It will be invaluable reading for professional practitioners graduate students, researchers and alike.
Book Synopsis Handbook of Intelligent Vehicles by : Azim Eskandarian
Download or read book Handbook of Intelligent Vehicles written by Azim Eskandarian and published by Springer. This book was released on 2012-02-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Intelligent Vehicles provides a complete coverage of the fundamentals, new technologies, and sub-areas essential to the development of intelligent vehicles; it also includes advances made to date, challenges, and future trends. Significant strides in the field have been made to date; however, so far there has been no single book or volume which captures these advances in a comprehensive format, addressing all essential components and subspecialties of intelligent vehicles, as this book does. Since the intended users are engineering practitioners, as well as researchers and graduate students, the book chapters do not only cover fundamentals, methods, and algorithms but also include how software/hardware are implemented, and demonstrate the advances along with their present challenges. Research at both component and systems levels are required to advance the functionality of intelligent vehicles. This volume covers both of these aspects in addition to the fundamentals listed above.
Book Synopsis Modeling Human Driving from Demonstrations by : Raunak Pushpak Bhattacharyya
Download or read book Modeling Human Driving from Demonstrations written by Raunak Pushpak Bhattacharyya and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: For autonomous agents to coexist and cooperate with humans, it is important for them to anticipate human behavior. Models of human behavior can be used by autonomous agents to plan in response to human actions and proactively coordinate with humans. Such models can also be used to build simulators for testing autonomous agents by replicating the environment of operation. Modeling human behavior is challenging because of multiple reasons such as stochasticity, multi-modality, unobservable intents, high dimensional state action spaces, and nonlinear dynamics. In the literature, both ontological and phenomenological approaches have been used to model human behavior. While ontological approaches use rules, phenomenological approaches are data-driven. This thesis presents techniques to model human behavior from demonstrations. The techniques proposed in this thesis are evaluated on their ability to model human driving behavior which is important in autonomous driving for both planning and safety validation. First, this thesis adapts the technique of Generative Adversarial Imitation Learning to the problem of driver modeling. It extends the GAIL formulation to work in the multi-agent setting where observations gathered from multiple agents are used to inform the training process of a learning agent. The proposed method is shown to better imitate demonstrated driving as opposed to single agent learning method. Since driving has associated rules, the second part of this thesis introduces a method to provide domain knowledge to the imitation learning agent through reward augmentation. The proposed method, which relies on reward augmentation, is shown to provide better emergent driving performance and overall traffic flow in the recreated traffic simulations. Many of the applications of autonomous agents are safety-critical including that of autonomous driving. This makes it important for models to be interpretable. This thesis proposes a hybrid rule-based and data-driven method that relies on the technique of particle filtering to learn parameters of underlying rule-based models from human driving demonstrations. The proposed method is demonstrated on the problem of highway merging and shown to generate realistic driving behavior as assessed by a driving Turing test.
Book Synopsis Human Behavior and Traffic Safety by : Leonard Evans
Download or read book Human Behavior and Traffic Safety written by Leonard Evans and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the papers and discussions from a Symposium on :'Hu man Behavior and Traffic Safety" held at the General Motors Research Labora tories on September 23-25, 1984. This Symposium was the twenty-ninth in an annual series sponsored by the Research Laboratories. Initiated in 1957, these symposia have as their objective the promotion of the interchange of knowledge among specialists from many allied disciplines in rapidly developing or chang ing areas of science or technology. Attendees characteristically represent the aca demic, government, and industrial institutions that are noted for their ongoing activities in the particular area of interest. of this Symposium was to focus on the role of human behavior The objective in traffic safety. In this regard, a clear distinction is drawn between, on the one hand, "human behavior," and on the other "human performance." Human per formance at the driving task, or what the driver can do, has been the subject of much research reported in the technical literature. Although clearly of some rel evance, questions of performance do not appear to be central to most traffic crashes. Of much more central importance is human behavior, or what the driver in fact does. This is much more difficult to determine, and is the subject of the Symposium.
Book Synopsis Modeling Human Driving Behavior by : Dimitrios Koutentakis
Download or read book Modeling Human Driving Behavior written by Dimitrios Koutentakis and published by . This book was released on 2020 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this thesis paper is to explore models that can predict and anticipate driver behaviors on the road and give probabilities on future actions of neighboring vehicles, while being lightweight enough to be formally verifiable. This thesis starts with looking into related work and doing a short literature review on previous work on driver models. We then talk about the available datasets used to perform such work, different models used (from classic regressions to neural networks) and finally present my approach and my results.
Book Synopsis Behavior Analysis and Modeling of Traffic Participants by : Xiaolin Song
Download or read book Behavior Analysis and Modeling of Traffic Participants written by Xiaolin Song and published by Morgan & Claypool Publishers. This book was released on 2021-12-01 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: A road traffic participant is a person who directly participates in road traffic, such as vehicle drivers, passengers, pedestrians, or cyclists, however, traffic accidents cause numerous property losses, bodily injuries, and even deaths to them. To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology nowadays. It is of great significance to the decision making and planning of a vehicle if the pedestrians' intentions and future trajectories, as well as those of surrounding vehicles, could be predicted, all in an effort to increase driving safety. Based on the image sequence collected by onboard monocular cameras, we use the Long Short-Term Memory (LSTM) based network with an enhanced attention mechanism to realize the intention and trajectory prediction of pedestrians and surrounding vehicles. However, although the fully automatic driving era still seems far away, human drivers are still a crucial part of the road‒driver‒vehicle system under current circumstances, even dealing with low levels of automatic driving vehicles. Considering that more than 90 percent of fatal traffic accidents were caused by human errors, thus it is meaningful to recognize the secondary task while driving, as well as the driving style recognition, to develop a more personalized advanced driver assistance system (ADAS) or intelligent vehicle. We use the graph convolutional networks for spatial feature reasoning and the LSTM networks with the attention mechanism for temporal motion feature learning within the image sequence to realize the driving secondary-task recognition. Moreover, aggressive drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Thus, we will focus on the driving style classification for the longitudinal car-following scenario. Also, based on the Structural Equation Model (SEM) and Strategic Highway Research Program 2 (SHRP 2) naturalistic driving database, the relationships among drivers' demographic characteristics, sensation seeking, risk perception, and risky driving behaviors are fully discussed. Results and conclusions from this short book are expected to offer potential guidance and benefits for promoting the development of intelligent vehicle technology and driving safety.
Book Synopsis Analysis of Driver Behavior Modeling in Connected Vehicle Safety Systems Through High Fidelity Simulation by : Ahura Jami
Download or read book Analysis of Driver Behavior Modeling in Connected Vehicle Safety Systems Through High Fidelity Simulation written by Ahura Jami and published by . This book was released on 2018 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: A critical aspect of connected vehicle safety analysis is understanding the impact of human behavior on the overall performance of the safety system. Given the variation in human driving behavior and the expectancy for high levels of performance, it is crucial for these systems to be flexible to various driving characteristics. However, design, testing, and evaluation of these active safety systems remain a challenging task, exacerbated by the lack of behavioral data and practical test platforms. Additionally, the need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly and time-consuming. As an alternative option, researchers attempt to use simulation platforms to study and evaluate their algorithms. In this work, we introduce a high fidelity simulation platform, designed for a hybrid transportation system involving both human-driven and automated vehicles. We decompose the human driving task and offer a modular approach in simulating a large-scale traffic scenario, making it feasible for extensive studying of automated and active safety systems. Furthermore, we propose a human-interpretable driver model represented as a closed-loop feedback controller. For this model, we analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics. Finally, we recreate a similarly dense traffic scenario within our simulator and conduct a thorough analysis of different human-specific and system-specific factors and study their effect on the performance and safety of the traffic network.
Book Synopsis Traffic Safety and Human Behavior by : David Shinar
Download or read book Traffic Safety and Human Behavior written by David Shinar and published by Emerald Group Publishing. This book was released on 2017-06-22 with total page 1262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive 2nd edition covers the key issues that relate human behavior to traffic safety. In particular it covers the increasing roles that pedestrians and cyclists have in the traffic system; the role of infotainment in driver distraction; and the increasing role of driver assistance systems in changing the driver-vehicle interaction.
Book Synopsis Modeling Human and Organizational Behavior by : National Research Council
Download or read book Modeling Human and Organizational Behavior written by National Research Council and published by National Academies Press. This book was released on 1998-08-31 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simulations are widely used in the military for training personnel, analyzing proposed equipment, and rehearsing missions, and these simulations need realistic models of human behavior. This book draws together a wide variety of theoretical and applied research in human behavior modeling that can be considered for use in those simulations. It covers behavior at the individual, unit, and command level. At the individual soldier level, the topics covered include attention, learning, memory, decisionmaking, perception, situation awareness, and planning. At the unit level, the focus is on command and control. The book provides short-, medium-, and long-term goals for research and development of more realistic models of human behavior.
Book Synopsis Handbook of Driver Assistance Systems by : Hermann Winner
Download or read book Handbook of Driver Assistance Systems written by Hermann Winner and published by Springer. This book was released on 2015-10-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This fundamental work explains in detail systems for active safety and driver assistance, considering both their structure and their function. These include the well-known standard systems such as Anti-lock braking system (ABS), Electronic Stability Control (ESC) or Adaptive Cruise Control (ACC). But it includes also new systems for protecting collisions protection, for changing the lane, or for convenient parking. The book aims at giving a complete picture focusing on the entire system. First, it describes the components which are necessary for assistance systems, such as sensors, actuators, mechatronic subsystems, and control elements. Then, it explains key features for the user-friendly design of human-machine interfaces between driver and assistance system. Finally, important characteristic features of driver assistance systems for particular vehicles are presented: Systems for commercial vehicles and motorcycles.
Book Synopsis Human-Like Decision Making and Control for Autonomous Driving by : Peng Hang
Download or read book Human-Like Decision Making and Control for Autonomous Driving written by Peng Hang and published by CRC Press. This book was released on 2022-07-25 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book details cutting-edge research into human-like driving technology, utilising game theory to better suit a human and machine hybrid driving environment. Covering feature identification and modelling of human driving behaviours, the book explains how to design an algorithm for decision making and control of autonomous vehicles in complex scenarios. Beginning with a review of current research in the field, the book uses this as a springboard from which to present a new theory of human-like driving framework for autonomous vehicles. Chapters cover system models of decision making and control, driving safety, riding comfort and travel efficiency. Throughout the book, game theory is applied to human-like decision making, enabling the autonomous vehicle and the human driver interaction to be modelled using noncooperative game theory approach. It also uses game theory to model collaborative decision making between connected autonomous vehicles. This framework enables human-like decision making and control of autonomous vehicles, which leads to safer and more efficient driving in complicated traffic scenarios. The book will be of interest to students and professionals alike, in the field of automotive engineering, computer engineering and control engineering.
Book Synopsis Development of Personalized Lateral and Longitudinal Driver Behavior Models for Optimal Human-vehicle Interactive Control by : Scott C. Schnelle
Download or read book Development of Personalized Lateral and Longitudinal Driver Behavior Models for Optimal Human-vehicle Interactive Control written by Scott C. Schnelle and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced driver assistance systems (ADAS) are a subject of increasing interest as they are being implemented on production vehicles and also continue to be developed and researched. These systems need to work cooperatively with the human driver to increase vehicle driving safety and performance. Such a cooperation requires the ADAS to work with the specific driver with some knowledge of the human driver’s driving behavior. To aid such cooperation between human drivers and ADAS, driver models are necessary to replicate and predict human driving behaviors and distinguish among different drivers. This dissertation presents several lateral and longitudinal driver models developed based on human subject driving simulator experiments that are able to identify different driver behaviors through driver model parameter identification. The lateral driver model consists of a compensatory transfer function and an anticipatory component and is integrated with the design of the individual driver’s desired path. The longitudinal driver model works with the lateral driver model by using the same desired path parameters to model the driver’s velocity control based on the relative velocity and relative distance to the preceding vehicle. A feedforward component is added to the feedback longitudinal driver model by considering the driver’s ability to regulate his/her velocity based on the curvature of his/her desired path. This interconnection between the longitudinal and lateral driver models allows for fewer driver model parameters and an increased modeling accuracy. It has been shown that the proposed driver model can replicate individual driver’s steering wheel angle and velocity for a variety of highway maneuvers. The lateral driver model is capable of predicting the infrequent collision avoidance behavior of the driver from only the driver’s daily driving habits. This is important due to the fact that these collision avoidance maneuvers require high control skills from the driver and the ADAS intervention offers the most benefits, but they happen very infrequently so previous knowledge of driver behavior during these incidents cannot be assumed to be known. The contributions of this dissertation include 1) an anticipatory and compensatory lateral driver steering model capable of modeling a wide range of in-city and highway maneuvers at a variety of speeds, 2) the combination of the lateral driver model with the addition of defining an individual driver’s desired path which allows for increased modeling accuracy, 3) a predictive lateral driver model that can predict a driver’s collision avoidance steering wheel angle signal with no prior knowledge of the driver’s collision avoidance behavior, only data from every day, standard driving, 4) the addition of a longitudinal driver model that works with the existing lateral driver model by using the same desired path and is capable of replicating an individual driver’s standard highway and collision avoidance behavior, and 5) A feedforward longitudinal driver model based on regulating the driver’s velocity along his/her desired path is added to the existing feedback longitudinal driver model that together are capable of modeling an individual driver’s velocity for lane-changing and collision-avoidance maneuvers with less than 0.45 m/s (1 mph) average error.
Book Synopsis Modeling Stochastic Human-driver Car Following Behavior in Oscillatory Traffic Conditions by : Raphael Stern
Download or read book Modeling Stochastic Human-driver Car Following Behavior in Oscillatory Traffic Conditions written by Raphael Stern and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurately modeling the realistic and unstable traffic dynamics of human-driven traffic flow is crucial to being able to understand how traffic dynamics evolve, and how new agents such as autonomous vehicles might influence traffic flow stability. This work is motivated by a recent dataset that allows us to calibrate accurate models, specifically in conditions when traffic waves arise. Three microscopic car-following models are calibrated using a microscopic vehicle trajectory dataset that is collected with the intent of capturing oscillatory driving conditions. For each model, five traffic flow metrics are constructed to compare the flow-level characteristics of the simulated traffic with experimental data.
Book Synopsis Behavior Modeling and Motion Planning for Autonomous Driving Using Artificial Intelligence by : Meixin Zhu
Download or read book Behavior Modeling and Motion Planning for Autonomous Driving Using Artificial Intelligence written by Meixin Zhu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With an emphasis on longitudinal driving, this dissertation aims to develop data-driven models that improve existing driving behavior models and facilitate various kinds of autonomous driving planning. The first part of this work focuses on behavior modeling, which falls within the background of microscopic traffic simulation, traffic flow theory, and motion prediction. Two different driving behavior models are proposed. To model the long-term dependency of future actions on historical driving situations, a long-sequence car-following trajectory prediction model is developed using the attention-based Transformer model. The model follows a general format of encoder-decoder architecture. The encoder takes historical speed and spacing data as inputs and forms a mixed representation of historical driving context using multi-head self-attention. The decoder takes the future lead vehicle speed profile as input and outputs the predicted future following speed profile in a generative way (instead of an auto-regressive way, avoiding compounding errors). The second part of this work extends the single forward-pass of behavior prediction in the first part to the sequential motion planning of autonomous driving. Based on different demands, two motion planning algorithms are proposed for autonomous longitudinal driving. To learn a driving policy that can do closed-loop sequential planning and imitate human drivers' behavior, a framework for human-like autonomous car-following planning based on deep reinforcement learning (RL) is proposed. Car-following dynamics are encoded into a simulation environment, and a reward function that signals how much the agent deviates from the empirical data is used to encourage behavioral imitation. It was found that using RL for imitation learning purposes can well address the distribution shift issue. This is the first study that uses RL to address the distribution shift issue for imitation-orientated longitudinal motion planning. To propose a safe, efficient, and comfortable velocity planning method for autonomous driving, a multi-objective velocity planning method based on RL is proposed. To directly optimize driving performance, a reward function is developed by referencing human driving data and combining driving features related to safety, efficiency, and comfort. It was found that the proposed model demonstrates the capability of safe, efficient, and comfortable velocity control and outperforms human drivers.
Book Synopsis Modeling Driver Behavior and Their Interactions with Driver Assistance Systems by : Ning Li
Download or read book Modeling Driver Behavior and Their Interactions with Driver Assistance Systems written by Ning Li and published by . This book was released on 2019 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: As vehicle automation becomes increasingly prevalent and capable, drivers have the opportunity to delegate primary driving task control to automated systems. In recent years, significant efforts have been placed on developing and deploying Advanced Driver Assistance Systems (ADAS). These systems are designed to work with human drivers to increase vehicle safety, control, and performance in both ordinary and emergent situations. Current ADAS are mainly presented in rule-based or manually programmed design based on the summary and modeling of pre-collected human performance data. However, the pre-fixed system with limited personalization may not match human drivers' needs, which may arise the driver's dissatisfaction and cause ineffective system improvement. Human-centered machine learning (HCML) includes explicitly recognizing this human operator's role, as well as re-constructing machine learning workflows based on human working practices. The goal of this dissertation is to build a novel driver behavior modeling framework to understand and predict interactions with the driver assistance system from a human-centered perspective. It can lead not only to more usable machine learning tools but to new ways of improving the driver assistance systems. A driving simulator study was conducted to evaluate drivers' interactions with Forward Collision Warning (FCW) system. Gaussian Mixture Model (GMM) clusterization was used to identify different driving styles based drivers' driving performance, secondary task engagement, eye glance behavior and survey information. The impact of the FCW system on the different driving styles was also evaluated and discussed from three perspectives: initial reaction, distraction types, and safety benefits. A driver behavior model was also built using inverse reinforcement learning. Lastly, the timing prediction of FCW using driving preference was compared to the algorithm from a traditional FCW system. The findings of this study showed that ADAS without human feedback may not always bring positive safety benefits. Learning driver's preference through inverse reinforcement learning could better account for future scenarios and better predict driver behavior (e.g., braking action). This algorithm can be incorporated into real world in-vehicle warning systems such that the feedback and driving styles of the human operator are appropriately considered.
Book Synopsis Traffic Flow Dynamics by : Martin Treiber
Download or read book Traffic Flow Dynamics written by Martin Treiber and published by Springer Science & Business Media. This book was released on 2012-10-11 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a comprehensive and instructive coverage of vehicular traffic flow dynamics and modeling. It makes this fascinating interdisciplinary topic, which to date was only documented in parts by specialized monographs, accessible to a broad readership. Numerous figures and problems with solutions help the reader to quickly understand and practice the presented concepts. This book is targeted at students of physics and traffic engineering and, more generally, also at students and professionals in computer science, mathematics, and interdisciplinary topics. It also offers material for project work in programming and simulation at college and university level. The main part, after presenting different categories of traffic data, is devoted to a mathematical description of the dynamics of traffic flow, covering macroscopic models which describe traffic in terms of density, as well as microscopic many-particle models in which each particle corresponds to a vehicle and its driver. Focus chapters on traffic instabilities and model calibration/validation present these topics in a novel and systematic way. Finally, the theoretical framework is shown at work in selected applications such as traffic-state and travel-time estimation, intelligent transportation systems, traffic operations management, and a detailed physics-based model for fuel consumption and emissions.
Book Synopsis Advanced Driver Intention Inference by : Yang Xing
Download or read book Advanced Driver Intention Inference written by Yang Xing and published by Elsevier. This book was released on 2020-03-15 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
