Search

The introduction of automobiles into the world inculcated innovation in many aspects of engineering, including design and manufacturing. Engineers worldwide continuously strive hard to develop cutting-edge technologies to augment the riders’ comfort, optimize traffic behavior, enhance safety and reduce fuel consumption. In the current scenario, advanced features, which include forward collision, traction control, adaptive cruise control, and lane change, augment safety. Along with these features, vehicle drive modes play a dual role of enhancing safety (e.g., traction control drive mode) and reducing energy consumption (e.g., fuel economy drive mode) in real-time. But a feature that ameliorates engine performance and optimizes the trip time was not investigated by the researchers until now. In this dissertation, a novel drive mode, “Intelligent Vehicle Drive Mode” (IVDM), was proposed, which augments the vehicle engine performance (VEP) in real time. In the current vehicle system, more than twenty drive modes are integrated that augment the safety and driver comfort, but none intervenes in the driver behavior vector (DBV). In this research, IVDM predicts the DBV, which optimizes the engine operating conditions (EOC). The metric of optimal EOC was defined using the vector engine operating point (EOP) and heating, ventilation, and air conditioning (HVAC) system. Deep learning (DL) models were developed by mapping the vehicle-level vectors (VLV) with EOP and HVAC parameters using real-time datasets obtained from the field tests performed using the Cadillac segment provided by General Motors Inc. The trained functions were utilized to predict the future states of DBV, reflecting augmented vehicle engine performance (VEP). An iterative analysis was performed by empirically estimating the future states of VLV in the allowable range of DBV and was fed into the DL model to predict the performance vectors. The defined vehicle engine performance (VEP) metric was applied to the predicted vectors, and thus optimal DBV is the instantaneous output of the IVDM. Finally, the proposed concept was quantified by analyzing the instantaneous engine efficiency (IEE) and the smoothness measure of the instantaneous engine map (IEM). Impact Statement: Real-time vehicle engine performance (VEP) is significantly affected by environmental conditions, HVAC systems, and the driver behavior vector (DBV). In this research, the featured vehicle drive mode that is integrated into the automotive system was the focus and can accommodate the user’s input of either activation or deactivation. Vehicle drive modes were developed to augment the rider’s comfort and safety and to reduce fuel consumption but not to intervene with the DBV, which is strictly the user’s prerogative. The proposed “Intelligent Vehicle Drive Mode” (IVDM) is embedded with the functionality of obliging the driver’s command in all scenarios and predicting the driver behavior vector (DBV) to enhance the vehicle engine performance (VEP) without increasing the time of trip traversal. The IVDM accommodates two user inputs [range of speeds, range of cabin temperatures] which is utilized to predict the optimal DBV. Also, IVDM can be activated as a stand-alone application or in conjunction with any other drive modes, accommodating a vehicle speed > 25 MPH on a regular terrain profile under normal driving conditions. The IVDM, which possesses the unique capability of optimizing the [fuel consumption, trip time], could emerge as a new feature of the automotive system and is most applicable to vehicles with built-in advanced driver assistance, infotainment, and connectivity features.

The morning rush in two idealized settings are forecast into the future, assuming that most drivers will engage Adaptive Cruise Control (ACC) while driving their cars. To this end, careful measurements of ACC-equipped cars traveling on roads and a highway confirm an earlier finding reported by others: for a given traffic speed in ACC-rich congestion, the density tends to be smaller than in present-day congestion where all vehicles are manually operated. All else equal, the lower congested densities (i.e., larger car spacings), mean that queues will be less-densely-packed and will expand over longer physical distances in the future, as ACC-equipped vehicles become more prevalent. These uncompacted queues pose problems for urban areas, where queue storage is often already a problem during rush hours.Simulations calibrated using the field-measured data confirm this concern and contradict optimistic predictions of how ACC may lead to a congestion-free future. In a setting with already moderately high congestion and long-street links inspired by Downtown Los Angeles, it is predicted that networkwide vehicle hours traveled (VHT) will increase by up to 12% compared to present-day levels, despite assumptions that are largely favorable to ACC. In a setting inspired by Midtown Manhattan, with short-street links and already high congestion levels, networkwide VHT is predicted to increase by as much as 87%. The higher bottleneck capacities often promised by advocates of ACC are shown to be irrelevant when spillover queues restrict flows from reaching those capacities. Interventions tested through simulations include adjusting onboard ACC controllers to produce lower jam spacings, to prevent a problematic future.

The primary contribution is the development of the data collection testing methodology for autonomous driving systems of a hybrid electric passenger vehicle. As automotive manufacturers begin to develop adaptive cruise control technology in vehicles, progress is being made toward the development of fully-autonomous vehicles. Adaptive cruise control capability is classified into five levels defined by the Society of Automotive Engineering. Some vehicles under development have attained higher levels of autonomy, but the focus of most commercial development is Level 2 autonomy. As the level of autonomy increases, the sensor technology becomes more advanced with a sensor suite which includes radar, camera, and vehicle-to-everything radio. Sensors must detect the objects around the vehicle to be able for communicate the data to the adaptive cruise control algorithm. If a vehicle is in an accident, the driver is typically responsible for the damages, but with an autonomous vehicle, there might not be a driver. A process to guarantee a vehicle will perform as it was developed is critical to a vehicle’s development and testing. The goal of this work is to implement a verification and validation system that can be used on adaptive cruise control systems. The system developed in this paper used different testing environments such as model-in-the-loop, hardware-in-the-loop, and vehicle-in-the-loop, to fully validate an autonomous vehicle. A systematic data acquisition process has been developed to support autonomous vehicle development. The data that was taken had an organized way of comparing the results in each environment. Requirements management, vehicle logbook, and test case creation played a vital role in combining the information across the environments. The method produced a consumer-ready adaptive cruise control system in a 2019 Chevrolet Blazer RS. The vehicle was able to perform at an Advanced Vehicle Technology Competition where the adaptive cruise control system placed 1st in Connected and Automated Vehicle Perception System & Adaptive Cruise Control Drive Quality Evaluation. Results are presented that illustrate the utility of the data acquisition and multi-layer testing process for autonomous vehicle development.

The Hybrid Electric Vehicle Team (HEVT) at Virginia Tech participates in the 4-Year EcoCAR Mobility Challenge organized by Argonne National Laboratory. The objective of this competition is to modify a stock 2019 internal combustion engine Chevrolet Blazer and incorporate a hybrid powertrain and advanced driver assist systems. The Blazer has a P4 hybrid architecture which contains an electric traction motor on the rear axle and an internal combustion engine on the front axle. HEVT seeks to develop a vehicle with advanced driving capabilities to demonstrate energy savings by utilizing existing technologies. The hybrid market has generally been tailored to small compact vehicles however, a Chevrolet Blazer is a midsize utility vehicle that offers additional space with the benefit of increased fuel economy. The research discussed in this paper focuses on the design of a Signalized Intersection Control Strategy. First, research is performed on different methods of intersection control and implementation with an existing Model Predictive Adaptive Cruise Controller. Based on ease of integration into an existing tuned Eco Adaptive Cruise Control System (ACC), a control strategy operating in the background of the main vehicle controllers is chosen. The main topic of this research is the development and simulation of a Signalized Intersection Control Strategy that works through an Eco ACC system to achieve further energy savings during an approach to a connected intersection while ensuring rider safety. This paper expands on the current knowledge of vehicle utilization of Signal Phase and Timing (SPaT) signals through simulated test cases of a vehicle system model using MATLAB. In each case, the tractive energy consumption and travel times are analyzed for both the Eco ACC system with Signalized Intersection Control Strategy (informed) vehicle and an assumed uninformed driver for comparison. In the case of a vehicle approaching a green intersection which turns red several seconds after SPaT information is received, the informed system shows a 92% decrease or 75 Wh/mi reduction in propel energy consumption at when compared to an uninformed driver. However, in a similar case where the vehicle accelerates back to cruising speed after the light turns green, displays only an 11% decrease or 47 Wh/mi reduction in propel energy consumption at the wheel when compared to the uninformed driver. These simulations confirm that the Signalized Intersection Control Strategy reduces the propel energy consumption at the wheel during approaches to signalized intersections without extending the travel time greatly and in some cases at all. The results of this research show that the control strategy reduces tractive energy consumption while maintaining travel time.

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is partaking in the 4-Year EcoCar Mobility Challenge organized by Argonne National Labs. The objective of this competition is to modify a stock 2019 traditional internal combustion engine Chevrolet Blazer and to transform the vehicle into a P4 hybrid. Due to the P4 Hybrid architecture, the HEVT vehicle has an internal combustion engine on the front axle and an electric motor on the rear axle. The goal of this competition is to create a vehicle that achieves better fuel economy and increases customer appeal. The general target market of hybrids is smaller vehicles. As a midsize sport utility vehicle (SUV), the Blazer offers a larger vehicle with the perk of better fuel economy. In the competition, the vehicle is assessed on the ability to integrate advanced vehicle technology, improve consumer appeal, and provide comfort for the passenger. The research of this paper is centered around the design of a full range longitudinal Adaptive Cruise Control (ACC) algorithm. Initially, research is conducted on various linear and nonlinear control strategies that provide the necessary functionality. Based on the ability to predict future time instances in an optimal method, the Model Predictive Control (MPC) algorithm is chosen and combined with other standard control strategies to create an ACC system. The main objective of this research is the implementation of Adaptive Cruise Control features that provide comfort and energy savings to the rider while maintaining safety as the priority. Rider comfort is achieved by placing constraints on acceleration and jerk. Lastly, a proper energy analysis is conducted to showcase the potential energy savings with the implementation of the Adaptive Cruise Control system. This implementation includes tuning the algorithm so that the best energy consumption at the wheel is achieved without compromising vehicle safety. The scope of this paper expands on current knowledge of Adaptive Cruise Control by using a simplified nonlinear vehicle system model in MATLAB to simulate different conditions. For each condition, comfort and energy consumption are analyzed. The city 505 simulation of a traditional ACC system show a 14% or 42 Wh/mi reduction in energy at the wheel. The city 505 simulation of the environmentally friendly ACC system show a 29% or 88 Wh/mi reduction in energy at the wheel. Furthermore, these simulations confirm that maximum acceleration and jerk are bounded. Specifically, peak jerk is reduced by 90% or 8 m/s3 during a jerky US06 drive cycle. The main objective of this analysis is to demonstrate that with proper implementation, this ACC system effectively reduces tractive energy consumption while improving rider comfort for any vehicle.

The EcoCAR Mobility Challenge is a student competition that tasks universities across North America with the hybridization and SAE Level 2 automation of a 2019 Chevy Blazer. In years 2 and 3 of the competition, the Ohio State EcoCAR team committed considerable effort to the development of an adaptive cruise control (ACC) feature. This paper provides a detailed discussion of what motivated the selection of a modified PID controller as the control method of choice for ACC. The state flow used by the team to achieve independent distance and velocity control is also reviewed. After designing the controller, the team performed particle swarm optimization to identify the ideal proportional, integral, and derivative gain values. In doing so, the team managed to greatly reduce maximum acceleration, RMS acceleration, and maximum jerk in simulation. While doing so, the efficiency of the vehicle was also improved by 8.45 percent. Then, in order to validate the real-world performance of the novel adaptive cruise controller, the team conducted a full range of anything-in-the-loop (XIL) testing. Across model, hardware, and vehicle closed-loop testing, Ohio State identified and resolved numerous potential issues in the controller and its implementation in the vehicle. Additionally, the safety and comfort of the ACC feature were verified across all testing environments, affirming the fidelity of the model and preparing the team for in-vehicle testing. Lastly, using a real target vehicle and live sensor data, Ohio State performed approach tests that demonstrate the functionality of its ACC in a real-world environment.

The objective of this work is to benchmark Advanced Driver Assistance Systems (ADAS) using a limited motion Driver-in-the-Loop (DiL) Simulator and Model Predictive Control (MPC). These ADAS features include Adaptive Cruise Control (ACC), Lane Keeping Assist (LKA), Forward Collision Warning (FCW), and Automatic Emergency (AEB). The handoff of these features is at the discretion of the driver but maintains the operational design domain. Simplified internal models for the ACC MPC and LKA MPC are presented, so the MPC toolbox CasADi could be used as Software-in-the-Loop (SiL). Both MPC’s leverage adaptive linear techniques alleviating the inherent nonlinearities. SiL ensures robust, real-time execution of the features integrating with the simulator.Regulators and automotive manufacturers are tasked with eliminating automotive deaths and injuries. Of active safety tools at their disposal, ADAS features provide a promising ability to aiding this cause. Driving simulators are becoming an important development tool for active safety systems, automated driving features, and vehicle dynamics development. This driving simulator couples SCANeR Studio®, CarSim®, and MATLAB/Simulink®. Refined and custom cues give the driver a sense of the virtual world providing the immersion. Offline verification using the testbed and sample results using the driving simulator shows the efficacy of prototyping and evaluating ADAS features using the simulator. Combining these elements allows for both quantitative and qualitative assessment of the systems’ functionality, performance, and safety assurance.

A vehicle-to-vehicle follower design based on RC Unmanned Ground Vehicle (UGV) is presented in this thesis. To achieve the desired performance for two-vehicle leader-follower, a 3DOF path trajectory tracking controller with a close-loop guidance controller are used, which consider both the kinematics and the dynamics characteristics of the vehicle model. In our research, we use a Trajectory Linearization Control (TLC) to achieve the path tracking, and a PID controller to guide the preceding vehicle. To this end, the following objectives have been achieved. First, a 3DOF kinematics and dynamics vehicle model has been built. Second, an Adaptive Cruise Control (ACC) Trajectory Linearizaton Control (ACCTLC) scheme is presented. Third, a Vehicle-to-Vehicle following Trajectory Linearizaiton Control is proposed. MATLAB/SIMULINK simulation testing of 3DOF control algorithm is presented, which verifies the algorithm. Future work include implementing the current controller design by installing those algorithms to the real RC car; as well as adding lane constraint to the current work; and adding obstacle avoidance to develop fully autonomous ground vehicle.

The number of vehicles on the road with advanced and automated driving support systems (DSSs) is increasing. However, there may be some issues related to the implementation of DSSs in vehicles. One of those issues caused by the automated DSSs relates to the drivers' being out-of-the-loop. As drivers' roles are transitioned from system operators to systems supervisors (as in autonomous vehicles), drivers' situation awareness of the driving surroundings may decrease which could negatively affect their responses when they need to take control of the vehicle from the malfunctioned (or failed) DSSs. Additionally, with both the adaptive cruise control (ACC) and lane keeping (LK) systems engaged, the longitudinal and lateral positions of the vehicle are under the control of automation and the vehicle becomes a semi-autonomous vehicle (i.e., the vehicles are now at level 2 automation based on the definitions of the National Highway Traffic Safety Administration taxonomy for automation). In semi-autonomous vehicles, drivers are more likely to interact with non-driving tasks and engage in risky behaviors (e.g., long glances away from the forward road way), as the demand of the driving tasks is much lower than manually driving and driving with only ACC engaged. This may worsen drivers' responses to the failures of semi-autonomous vehicle components, when drivers are engaged in non-driving tasks. The objectives of this dissertation were to assess how drivers respond to the failures of the LK system with different levels of vehicle automation and to assess the effects of drivers' engagements in non-driving tasks on their behaviors associated with a failure of the LK system. This dissertation also investigates if a lane departure warning would mitigate the negative effects of out-of-the-loop problem brought on by automation and improve drivers' responses to the LK system fails especially when drivers are engaging both the ACC and LK systems. Additionally, the relationships between drivers' personalities and attitudes toward automation and their responses during the failure of the LK system were evaluated. Three experiments were used to address the dissertation research objectives. The results demonstrate that drivers in semi-autonomous vehicles (level 2 automation vehicles) have less safe behaviors (e.g., more engagement in non-driving tasks and longer glances away from the roadway) than their peers who were manually driving the vehicles. During the failures of the LK systems, drivers in semi-autonomous vehicles have worse driving behaviors compared to their counterparts driving manually or driving with the LK system engaged. Non-driving tasks also increase drivers' reaction time to safety critical events in semi-autonomous vehicles. However, the effects of audible lane departure warnings on drivers' responses to potential lane departure events were not consistent between the level 0 automation condition (i.e., the manual driving condition) and level 2 automation condition (i.e., the automated driving condition). Overall, audible warnings with 1.48 s prediction time assist drivers' in responding to the lane departure events following the failure of the LK system in semi-autonomous vehicles. However, the effects of audible warnings on drivers' responses to the potential lane departure events are divergent when drivers are manually operating the vehicles. Though audible warnings as one type of discrete feedback of automation activities help drivers improve their responses to safety critical events in semi-autonomous vehicles, they cannot solve the out-of-control loop problem caused by automation. Future work should evaluate if continuous feedback could address the out-of-control loop problem brought on by automation and keep drivers in the vehicle control loop in semi- or fully- autonomous vehicles.

There are a large number of models that can be used to describe traffic flow. Although some were initially theoretically derived, there are many that were constructed with utility alone in mind. The Intelligent Driver Model (IDM) is a microscopic model that can be used to examine traffic behavior on an individual level with emphasis on the relation to an ahead vehicle. One application for this model is that it is easily molded to performing the operations for an Adaptive Cruise Control (ACC) system. Although it is clear that the IDM holds a number of convenient properties, like easily interpreted parameters, there is yet to be any rigorous examining of this model from a mathematical standpoint. This paper will place this model into the form of a vector-valued time-autonomous ODE system and analytically examine it. Additionally, the parameter estimation problem will be formulated. Simulations will demonstrate the model in practice.

Intelligent Transportation Systems (ITS) apply well-established technologies in communications, control, and computer hardware and software to increase safety and improve operational performance of the transportation network without expanding the current infrastructure. For many ITS applications, ensuring safety of the traffic participants, including drivers and pedestrians, is one of the most important research initiatives of the Intelligent Transportation Systems Society (ITSS). The ITS applications range from collision avoidance for autonomous or human-driven vehicles to cooperation of multiple vehicles to achieve common goals such as reduced fuel consumption or increased traffic throughput. The main challenges when designing controllers for such systems are the need to consider the close combination of, and coordination between, the system's computational and physical elements. Most of the vehicles nowadays are controlled by tens of or even hundreds of microcontrollers, which communicate via a CAN bus, for electric steering, braking, chassis and body control. Moreover, vehicles interact with other traffic participants including (semi) autonomous vehicles and human-driven cars and also with roadside units through a Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication, resulting in a large-scale Cyber-Physical System. Thus, traditional control theory that has been devoted to modeling continuous systems cannot adequately model such complex Cyber-Physical Systems, where both continuous (physical plant, e.g., vehicle) and discrete components (computing and communication) closely interacting each other.This thesis studies the design of continuous control laws that satisfy the safety property of the systems and their interfaces with discrete components that abstract human's high-level, decision making process. Our primary goals are to design continuous controllers for ITS applications that by design guarantee the safety property without further verification. First of all, hybrid systems, a class of modeling frameworks which form the foundation for a mathematical approach to Cyber-Physical Systems will be introduced. Then the reachability analysis techniques are developed to compute the exact reachable sets which will then be manipulated to design control laws that satisfy the safety property of the system.As a motivating application, we consider the Adaptive Cruise Control (ACC) system which becomes increasingly popular in commercial vehicles but lacks a fully-automated Collision Avoidance (CA) functionality, thus still leaving the responsibility to human drivers to apply proper braking force. Since the currently available ACC systems are developed mainly for providing drivers comfort and convenience riding, it cannot address the situation where safety should come first. Such situations may include sudden deceleration of a preceding vehicle and cut-in by a slower vehicle, where a rear-end collision is imminent or unavoidable. Thus, an active CA system needs to be developed and fully integrated into the ACC system in order to give more control authority to the vehicle when the driver cannot react fast enough to the imminent collision event.In this regard, the CA problem between the ACC-equipped vehicle (follower) and the preceding vehicle (leader) is formulated as a pursuit-evasion game to take into account the worst case scenario. That is, the leader tries to cause a collision by full braking while the follower tries to avoid it. By solving this game under the assumption that the follower's braking force is larger than that of the leader, the unsafe region can be obtained as a union of sets reachable from a collision set. This means that if the follower is within this unsafe region it cannot avoid a collision regardless of its control effort, i.e., the unsafe region becomes a controlled-invariant set when the leader plays optimally. In other words, the follower can always avoid a collision if it stays outside of the unsafe region for all time. The resulting safe set, complement of the unsafe region, represents the minimum safe distances and serves as a lookup table so that the follower can refer to it and apply an appropriate level of braking. Moreover, the ACC with CA system will be further augmented with a communication capability so that vehicles can interact with other vehicles and maintain a very small gap, preferable several meters, to the preceding vehicles. This can implement Cooperative Adaptive Cruise Control (CACC) for multiple vehicles forming a platoon with a small inter-vehicle spacing in highway traffic, resulting in reduced fuel consumption and increased traffic throughput.

According to a recent report by the US Treasury Department, America wastes $8 billion annually in energy costs because of traffic congestion. Adding the cost of lost time, the damage is said to reach around $100 billion. Moreover, high energy consumption adds to air pollution and contributes to the global warming problem. Infrastructure where different entities (cars and traffic signals) can communicate with each other offers the potential for reducing this waste. But by how much? Suppose full information about location, velocity, and acceleration of each vehicle were available for all vehicles in the vicinity of an isolated traffic signal. Could an intelligent traffic signal predict and adjust to the best possible traffic light cycle times to minimize fuel loss per vehicle? If light timing were changed dynamically based on real-time information from new traffic arrivals after a small interval of time, how much lower fuel loss could be achieved than by basing timing on macro-level metrics such as flow rates and limited vehicle information such as that provided by in-pavement loop detectors? Answering these questions involves developing a simulation framework that is based on an understanding of typical yet safe vehicle operation (by human drivers or autonomous vehicles) and of various traffic arrival patterns, as well as the ability to estimate fuel loss (and/or other optimization objectives) in many different situations.

In comparison to conventional cruise control, adaptive cruise control (ACC) vehicles are capable of sensing forward traffic and slowing to accommodate as necessary. When no forward vehicles are present, ACC function is the same as conventional cruise control. However, with ACC, when a slower vehicle is detected, the ACC system will decelerate and follow at a selected time-based distance. While slowing to follow, the driver will experience a system-controlled deceleration of the ACC vehicle. An experiment was conducted to evaluate driver preferences for the distance at which the primary deceleration occurs and the level of deceleration that is obtained. Driver intervention was required in one trial and driver response behavior was measured. Ten men and ten women in two age groups evaluated the decelerations from a cruise speed of 70mph to a following speed of 55mph behind a confederate lead vehicle on the highway. Evaluations can be made using four scales: Good vs. Bad, Comfortable vs. Uncomfortable, Jerky vs. Smooth, and Early vs. Late. Decelerations of approximately 0.06g which occur approximately 200ft to 250ft behind the lead vehicle were most preferred. Prior to intervention, foot position ranged from a point directly below the brake pedal to 16.4in from the brake pedal. Foot motion began between 21.12s time-to-collision (TTC) and 3.97s TTC. Eighty percent of the participants paused to "cover" the brake before final motion to activate the brake. The older age group intervened (braked) later than the younger age group. Driver braking after intervention ranged from 0.16g to 0.32g.