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.

This book presents works from world-class experts from academia, industry, and national agencies representing countries from across the world focused on automotive fields for in-vehicle signal processing and safety. These include cutting-edge studies on safety, driver behavior, infrastructure, and human-to-vehicle interfaces. Vehicle Systems, Driver Modeling and Safety is appropriate for researchers, engineers, and professionals working in signal processing for vehicle systems, next generation system design from driver-assisted through fully autonomous vehicles.

While human drivers are currently responsible for almost all of the transport of goods and people, autonomous vehicles are near-at-hand and are expected to replace human drivers in the next few decades. Several vehicle manufacturing and tech companies have begun testing their autonomous vehicles on public roads. During this period of testing, human drivers are employed to ensure the safety of the passengers and those in the immediate vicinity in the event of a failure of automation. Moreover, lower levels of automation, that are currently available to the general public, also require constant human supervision to ensure safety. This makes the study of the cognitive, behavioral and psycho-physiological state of drivers of automation crucial. In this thesis, I review existing literature on the study of driver state and behavior and on the methodologies employed by researchers to study them. I present a series of studies that investigate the changes in driver state while using automated vehicles. To this end, I employed various physio- logical sensors such as near-infrared spectroscopy, galvanic skin response, and electrocardiography. I argue the case for driver state monitoring in automated vehicles to ensure vigilance and driver availability for a take over of control. The first study I present investigates the reasons for the presence of sleepy and drowsy behavior in drivers of automated vehicles. I found that the reason for drowsy behavior was prolonged intervals of low cortical activity. The second and third study investigate the changes in cortical activity across different levels of automation and over time. These studies showed that drivers who used partially automated vehicles had the highest levels of cognitive activity and drivers of partial and fully automated vehicles showing significant decreases in cortical activity over time. In the last study, I employ a panel design to investigate the impact of prior and knowledge and training on driver preparedness and behavior. I found that drivers are prepared when they are trained on the possible failure modes of automation. However, this preparedness decreases over time in both drivers with and without any training. These studies add to the existing knowledge of driver state investigations while providing much needed insight into the longitudinal changes in driver state. It is my hope that this thesis provides the foundation for future investigations into training programs for safety drivers in autonomous vehicles and serve as guidance for developers of automated driving technology.

Autonomous Vehicles and Future Mobility presents novel methods for examining the long term effects on individuals, society, and on the environment on a wide range of forthcoming transport scenarios such self-driving vehicles, workplace mobility plans, demand responsive transport analysis, mobility as a service, multi-source transport data provision, and door-to-door mobility. With the development and realization of new mobility options comes change in long term travel behavior and transport policy. Autonomous Vehicles and Future Mobility addresses these impacts, considering such key areas as attitude of users towards new services, the consequences of introducing of new mobility forms, the impacts of changing work related trips, the access to information about mobility options and the changing strategies of relevant stakeholders in transportation. By examining and contextualizing innovative transport solutions in this rapidly evolving field, Autonomous Vehicles and Future Mobility provides insights into current implementation of these potentially sustainable solutions, serving as general guidelines and best practices for researchers, professionals, and policy makers. Covers hot topics including travel behavior change, autonomous vehicle impacts, intelligent solutions, mobility planning, mobility as a service, sustainable solutions, and more Examines up to date models and applications using novel technologies Contributions from leading scholars around the globe Case studies with latest research results

This SpringerBrief begins by introducing the concept of smartphone sensing and summarizing the main tasks of applying smartphone sensing in vehicles. Chapter 2 describes the vehicle dynamics sensing model that exploits the raw data of motion sensors (i.e., accelerometer and gyroscope) to give the dynamic of vehicles, including stopping, turning, changing lanes, driving on uneven road, etc. Chapter 3 detects the abnormal driving behaviors based on sensing vehicle dynamics. Specifically, this brief proposes a machine learning-based fine-grained abnormal driving behavior detection and identification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using the built-in motion sensors in smartphones. As more vehicles taking part in the transportation system in recent years, driving or taking vehicles have become an inseparable part of our daily life. However, increasing vehicles on the roads bring more traffic issues including crashes and congestions, which make it necessary to sense vehicle dynamics and detect driving behaviors for drivers. For example, sensing lane information of vehicles in real time can be assisted with the navigators to avoid unnecessary detours, and acquiring instant vehicle speed is desirable to many important vehicular applications. Moreover, if the driving behaviors of drivers, like inattentive and drunk driver, can be detected and warned in time, a large part of traffic accidents can be prevented. However, for sensing vehicle dynamics and detecting driving behaviors, traditional approaches are grounded on the built-in infrastructure in vehicles such as infrared sensors and radars, or additional hardware like EEG devices and alcohol sensors, which involves high cost. The authors illustrate that smartphone sensing technology, which involves sensors embedded in smartphones (including the accelerometer, gyroscope, speaker, microphone, etc.), can be applied in sensing vehicle dynamics and driving behaviors. Chapter 4 exploits the feasibility to recognize abnormal driving events of drivers at early stage. Specifically, the authors develop an Early Recognition system, ER, which recognize inattentive driving events at an early stage and alert drivers timely leveraging built-in audio devices on smartphones. An overview of the state-of-the-art research is presented in chapter 5. Finally, the conclusions and future directions are provided in Chapter 6.