In the progression towards SAE Level 4 automation, the functional safety of automated driving systems is deemed essential, especially in the event of faults. The ISO 26262 functional safety standard is utilized to evaluate the risks associated with malfunctions in electrical/electronic (E/E) systems, based on a subjective assessment by safety experts. Yet, this standard primarily relies on qualitative measures and lacks provisions for real-time risk estimation. In this thesis, a risk estimation methodology has been developed to fill this gap, offering a quantitative method suitable for real-time risk analysis. A diagnostic system has been created to supplement the existing onboard diagnostic modules provided by the OEM. This integration creates a dual-layer safety net, ensuring secure operation in autonomous mode and providing a reliable fallback to the human operator when required. The quantitative risk estimation model that calculates the probability of collision, accounts for sensor and actuator faults amid measurement uncertainties. Based on the estimated probability, fault behavior is dynamically classified into distinct risk regions. The system is designed to respond appropriately to the situation by tailoring mitigating actions from minor adjustments to fallback protocols based on the level of risk and the type of fault. The proposed framework is illustrated through scenario-based testing via multiple simulations and closed-course evaluation using the test vehicle. This research has been conducted to contribute towards OSU's team, Buckeye AutoDrive, participating in Year 3 of the SAE AutoDrive Challenge II.

Visual Odometry has been a popular research topic for the last 40 years. Odometry alone has shown robustness across different sensor modalities including LiDAR and vision. In the automated vehicle setting, VO can provide an alternative to sensors like GPS that are prone to large errors in the presence of urban canyons and poor weather. Recent approaches focus on deep learning and the estimation of uncertainty from forward-facing cameras. In this research, Visual Odometry from downward-facing cameras is revisited to produce a simple yet robust alternative to forward-facing methods. Visual Odometry from downward-facing cameras has applications across many robotic platforms including UAVs, AUVs, and UGVs due to the relative location of prominent features in their respective environments. However, in the automated vehicle setting, this is particularly challenging due to severe motion blur, variable lighting conditions, and varying texture. In this thesis, a comprehensive review of odometry approaches is discussed. Several approaches for estimating a camera's pose from a downward-facing camera were explored. And finally, a geometric Visual Odometry pipeline from a downward-facing camera is proposed.

In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies. This drive stemmed from the developments in communication technologies for Connected and Autonomous Vehicles (CAV), such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication. Coupled with automated driving capabilities of CAVs, a new and exciting era has started in the world of transportation as each transportation agent is becoming more and more connected. To keep up with the times, research in the academia and the industry has focused on utilizing vehicle connectivity for various purposes, one of the most significant being fuel savings. Motivated by this goal of fuel saving applications of Connected Vehicle (CV) technologies, the main focus and contribution of this dissertation is developing and evaluating a complete Eco-Driving strategy for CAVs. Eco-Driving is a term used to describe the energy efficient use of vehicles. In this dissertation, a complete and comprehensive Eco-Driving strategy for CAVs is studied, where multiple driving modes calculate speed profiles ideal for their own set of constraints simultaneously to save fuel as much as possible while a High Level (HL) controller ensures smooth transitions between the driving modes for Eco-Driving. The first step in making a CAV achieve Eco-Driving is to develop a route-dependent speed profile called Eco-Cruise that is fuel optimal. The methods explored to achieve this optimally fuel economic speed profile are Dynamic Programming (DP) and Pontryagin's Minimum Principle (PMP). Using a generalized Matlab function that minimizes the fuel rate for a vehicle travelling on a certain route with route gradient, acceleration and deceleration limits, speed limits and traffic sign (traffic lights and STOP signs) locations as constraints, a DP based fuel optimal velocity profile is found. The ego CAV (open full item for complete abstract)

Most vehicles on the road today are conventional vehicles which require the use of nonrenewable fuels to operate. Coupled with this need is a large amount of emissions released into the atmosphere throughout the duration of every trip. To alleviate the burden this places on the environment, governments worldwide have pushed for strict mandates which aim to reduce and, eventually, eliminate the use of fossil fuels. To meet government requirements, hybrid and electric vehicles have been the focus of many car manufacturers. Advancements in vehicle technology have significantly increased the potential of hybrid vehicle technology to reduce levels of emissions and fuel consumption. Advanced energy management strategies have been developed to properly handle the power flow through the vehicle powertrain. These range from rule-based approaches to globally optimal techniques such as dynamic programming (DP). However, cost of high-power computational hardware and lack of a-priori knowledge of future road conditions poses difficult challenges for engineers attempting to implement globally optimal frameworks. A viable solution to the problem is to leverage on-board sensors present in most vehicles equipped with basic advanced driver assistance systems (ADAS) to obtain a prediction of the future road conditions. Known as look-ahead predictive EMS, this approach partially solves the lack of a-priori knowledge since a detailed view of the road ahead is available. However, uncertainty in sensors and the computational burden of processing large amounts of data creates more difficulties. This research aims to address the challenges mentioned above. A look-ahead predictive EMS is proposed which combines the use of a globally optimal approach (DP) with the equivalent consumption minimization strategy (ECMS) to obtain an optimal solution for a future prediction horizon. ECMS is highly sensitive to the equivalence factor, s, making it necessary to adapt during a trip to account for dist (open full item for complete abstract)

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 Ohio State University has participated in Advanced Vehicle Technology Competitions (AVTCs) for over 21 years. These competitions challenge universities throughout North American to reengineer a vehicle with technologies advancing the automotive market. This work explores the use of systems engineering practices during the eleventh iteration of the AVTC program, the EcoCAR 3 competition. The document presents the systems engineering process and two case studies implementing the process. The systems engineering process presented is a simplification of the “Vee” and “Agile” systems engineering processes applicable to a high-cost, long-term, prototype program. The process is broken into five stages: Concept Creation and Refinement, Architecture and Metric Creation, Development, Verification, and Assessment and Validation. The two case studies present uses of the process at a low-level applied to a software algorithm and at a high-level applied to an entire project. The first case study reviews the development of a diagnostic algorithm for the automated manual transmission used in the EcoCAR 3 competition vehicle. The team automated a manual transmission and needed an algorithm to detect and isolate failures to components of the transmission system. The concept and requirements for this algorithm are detailed in Chapter 1 before continuing to discussion of development and testing. Testing of the algorithm utilizes a model-based environment. The second case study reviews the construction and execution of a behavioral study project evaluating driver performance during a vehicle to driver transition of an SAE Level 3 partially automated vehicle. Research was conducted in a model-based environment, simulating an autonomous vehicle by utilizing a driving simulator. The project requirements are derived from the applicable parent requirements, implemented, and tested.

Cooperative highway driving systems (CHDSs) consist of collaborating vehicles with automated control units and vehicle-to-vehicle communication capabilities. Such systems are proposed as an important component of intelligent transportation systems (ITS) aiming at improving energy efficiency and driving safety. CHDSs have a broad spectrum of applications, ranging from automated freight systems to highway automation to smart city transit. Modeling and control of cooperative vehicles on highways contributes importantly to CHDS development. This problem is of critical importance in developing safe and reliable controllers and establishing frameworks and criteria verifying CHDS performance. This work focuses on the cooperative control problems in developing CHDSs by investigating distributed model predictive control (DMPC) techniques. In particular, collaboration of connected and automated vehicles is first formulated into a constrained optimization problem. Then, different DMPC strategies are investigated considering features of the cooperative control problem in a CHDS. We focus on non-iterative DMPC schemes with partially parallel information exchange between subsystems. Feasibility and stability properties of the closed-loop system applying non-iterative DMPC are established taking into account the coupling of the control input with state predictions calculated at previous step. Furthermore, a non-iterative DMPC scheme implementing a partitioning procedure is proposed to reduce the conservatism of compatibility constraints while guaranteeing safe inter-vehicle distances. With the DMPC scheme controlling the connected and automated vehicles, we further investigate interactions of cooperative driving groups with surrounding human-operated vehicles in mixed traffic environments. A behavior classification framework is developed to detect driver behaviors of surrounding human-operated vehicles. With the behavior classification framework, a behavior-guided MPC controller (open full item for complete abstract)

The use of multiple sensors in modern day vehicular applications is necessary to provide a complete outlook of surroundings for advanced driver assistance systems (ADAS) and automated driving. The fusion of these sensors provides increased certainty in the recognition, localization and prediction of surroundings. A deep learning-based sensor fusion system is proposed to fuse two independent, multi-modal sensor sources. This system is shown to successfully learn the complex capabilities of an existing state-of-the-art sensor fusion system and generalize well to new sensor fusion datasets. It has high precision and recall with minimal confusion after training on several million examples of labeled multi-modal sensor data. It is robust, has a sustainable training time, and has real-time response capabilities on a deep learning PC with a single NVIDIA GeForce GTX 980Ti graphical processing unit (GPU).

This thesis focuses on the importance of early and continued testing for Intelligent Transportation System applications, utilizing simulation environments and scaled- down testbeds. By introducing complete end-to end testing procedures and illustrating these on state-of-the-art ITS algorithms, the relationship between different test platforms and scales are described in detail. Results from different scales, and the corresponding quality metrics are presented and compared. A low-cost and flexible supplement to full-scale ITS testing is presented through the use of small-scale testbeds, which reduces the time and effort spent on the testing stage of ITS system design and development. This allows the researchers to implement, compare, and assess different architectures for intelligent transportation by deploying hardware-in-the-loop (HIL) simulations and tests, and it gives strong indications on the performance and high-level behavior of such systems at full scale. A range of concepts were demonstrated at The Ohio State University Control and Intelligent Transpiration Research Laboratories. Detailed implementations of applications based on an autonomous parking system, stop sign precedence system, green light speed advisory system, and a collaborative vehicle tracking system are provided. Finally, the design, development, and implementation details for a novel testing and evaluation methodology for Lane Departure Warning and Prevention systems are discussed. Development and testing steps from computer simulations to full-scale vehicle experiments are presented.