Development of Personalized Lateral and Longitudinal Driver Behavior Models for Optimal Human-vehicle Interactive Control PDF Download

Author: Scott C. Schnelle
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
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.

Author: SeHwan Kim (Mechanical engineer)
Publisher:
ISBN:
Category : Automated vehicles
Languages : en
Pages :

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Book Description
Automotive research and technological development to date have enabled improvement in driving comfort, operational efficiency, motion stability and, most importantly, safety. An expectation for perfect or near-perfect vehicle system automation is increasing. However, the actual application of high-level technologies becomes more challenging as more advanced and complex technologies develop because the roadways in real life are comprised of countless uncertainties. The coexistence of automated vehicles and human-driven vehicles on roadways will be inevitable and drivers exhibit diversified driving habits and decision-making idiosyncrasies, behaving differently even in the same situation. As such, an appropriate understanding of human driver behavior in various driving situations would be beneficial. This dissertation is motivated by a research assumption that people’s driving behavior, even in crash imminent situations, can be predicted by analyzing a wide spectrum of daily driving data which also can be utilized in designing personalized control systems especially in crash imminent situations. This dissertation presents several applications of advanced control theories to replicate and to predict drivers’ longitudinal (i.e. speed control) and lateral (i.e. steering wheel control) control behaviors in various driving situations by utilizing the respective drivers’ historic driving data. In addition, optimally personalized control systems based on personalized situational evaluations in crash imminent situations are presented. Three test vehicles and a virtual reality driving simulator were used to collect driving data. In addition, extensive naturalistic driving data which include several crash and near-crash events were used for identifying driver characteristics. The simulation results showed that the proposed models are able to replicate driving data and predict individual driver’s preferred control inputs, and successfully control a vehicle that adapts to individual drivers in crash imminent situations. The contributions of this dissertation include: 1) an analysis of human driver behavior in various driving situations for driving characteristic identification; 2) development of a prediction model which is able to provide driver’s preferred control inputs; 3) development of a personalized crash-imminence detection model based on drivers’ historic driving data; 4) applications of control theories to develop optimally personalized control models in crash imminent situations; and 5) development of various metrics to evaluate driving situations for personalized decision-making. It is expected that the findings of this dissertation will benefit further research on vehicle stability and safety as well as more advanced vehicle technologies, particularly in personalization technologies.

Author: Peng Hang
Publisher: CRC Press
ISBN: 1000625028
Category : Mathematics
Languages : en
Pages : 237

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Book Description
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.

Author: Xiaolin Song
Publisher: Springer Nature
ISBN: 3031015096
Category : Technology & Engineering
Languages : en
Pages : 160

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Book Description
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.

Author: Hermann Winner
Publisher: Springer
ISBN: 9783319123516
Category : Technology & Engineering
Languages : en
Pages : 0

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Book Description
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.

Author: Lars Weber
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Models to simulate individual driver behavior offer the possibility to investigate human-machine interaction in early stages of driver assistance system development. Many driver behavior models were published with regard to the simulation of longitudinal and lateral vehicle control, but the number of detailed models which simulate gap acceptance behavior preceeding a lane change (overtaking) maneuver is comparably small. In this thesis, the influence of different rear view mirror types on driver's gap acceptance behavior during a simulated lane change scenario on a two-lane German Autobahn was investigated and a driver behavior model was built to simulate this behavior. Individual behavior differences were also considered. As a first step, a simulation of different rear view mirror types (Planar, Convex C20 / C14) was implemented in a research driving simulator. Afterwards, two driving simulator studies were conducted: 1) To validate the implementation of the three mirror types, the results of a distance estimation study were compared against already published field studies with real mirror. 2) Results of a lane change study were then used for the development of a driver behavior model to simulate gap acceptance behavior: to estimate gap size and closing speed of approaching vehicles from behind, the model relies on visual angles and their rate of change which are well known concepts in psychology of visual perception. engl.

Author: C.C. MACADAM
Publisher:
ISBN:
Category :
Languages : en
Pages : 292

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Author: Mudasser Seraj
Publisher:
ISBN:
Category : Automated vehicles
Languages : en
Pages : 0

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Book Description
The Introduction of Connected-Automated Vehicle (CAV) technology provided a new opportunity to fix the traditional transportation system. Automated vehicles (AuV) would take the driving responsibility and drive the vehicles by analyzing their' surrounding through a range of sensors. The connectivity feature of these vehicles would facilitate to sense of the roadway and traffic conditions beyond the range of sensors and make informed decisions. While the vehicles equipped with these technologies becoming more common, large-scale market penetration will take a long time. Hence, our transportation infrastructure will pass through a transitional phase where both Human-driven vehicles (HuV) and AuVs share the roadway. Additionally, the prosperity and acceptance of these technologies depend on a clear understanding of the implications of overcoming the limitations of the traditional transportation system. My research focused on developing a comprehensive modeling framework to establish numerical simulation of both types of vehicles (i.e., HuVs, AuVs ) while recognizing the variations of driving behaviors of human drivers. Modeling both vehicle types provided the opportunity to explore diverse mixed traffic scenarios to attain extensive insights into such traffic conditions. Prior to developing the modeling framework, the variations of the human driving patterns were identified through extensive analysis of real-world human driving data. Bi-directional (i.e., longitudinal, lateral) control features were analyzed to comprehend human instincts during driving which can be integrated with the human driver modeling. Further analysis was performed to classify driving behaviors based on these features for the short and long term. The upsides of studying human driving behavior rest not only on better understanding for modeling human drivers but also on designing automated vehicles capable of addressing the variations of human driver behavior. The behavioral classification approach in this part of the research used three vehicular features known as jerk, leading headway, and yaw rate to classify human drivers into two groups (Safe and Hostile Driving) on short-term classification, and drivers' habits are categorized into three classes (Calm Driver, Rational Driver, and Aggressive Driver). Through the proposed method, behavior classification has been successfully identified in 86.31 ± 9.84% of speeding and 87.92 ± 10.04% of acute acceleration instances. Afterward, the foundation of mixed traffic modeling was developed through car-following strategy formulation. This part of the research proposes a naïve microscopic car-following strategy for a mixed traffic stream in CAV settings and measured shifts in traffic mobility and safety as a result. Additionally, this part of the research explores the influences of platoon properties (i.e. Intra-platoon Headway, Inter-platoon Headway, Maximum Platoon Length) on traffic stream characteristics. Different combinations of HuVs and AuVs are simulated in order to understand the variations of improvements induced by AuVs in a traffic stream. Simulation results reveal that grouping AuVs at the front of the traffic stream to apply CACC-based car-following model will generate maximum mobility benefits for the traffic. Higher mobility improvements can be attained by forming long, closely spaced AuVs at the cost of reduced safety. To achieve balanced mobility and safety advantages from mixed traffic movements, dynamically optimized platoon configurations should be determined at varying traffic conditions and AuVs market penetrations. Finally, grounded on prior research on human driving behavior and modeling framework of mixed traffic, this research objectively experimented with bi-directional motion dynamics in a microscopic modeling framework to measure the mobility and safety implications for mixed traffic movement in a freeway weaving section. This part of research begins by establishing a multilane microscopic model for studied vehicle types from model predictive control with the provision to form a CACC platoon of AuV vehicles. The proposed modeling framework was tested first with HuV only on a two-lane weaving section and validated using standardized macroscopic parameters from the HCM. This model was then applied to incrementally expand the AuV share for varying inflow rates of traffic. Simulation results showed that the maximum flow rate through the weaving section was attained at a 65% AuV share while steadiness in the average speed of traffic was experienced with increasing AuV share. Finally, the results of simulated scenarios were consolidated and scaled to report expected mobility and safety outcomes from the prevailing traffic state as well as the optimal AuV share for the current inflow rate in weaving sections.

Author: Thanh Phuc Le
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Ning Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 125

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Book Description
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.