In recent years, significant progress has been made in autonomous robotics and the electrification of transportation, highlighting the growing importance of automation in daily life. Ensuring the safety and sustainability of automated systems necessitates the integration of intelligent algorithms capable of making astute decisions in uncertain circumstances. Autonomous robots possess considerable potential for efficiently performing intricate tasks, but this potential can only be unlocked through intelligent algorithms. Moreover, enhancing the energy efficiency of transportation systems yields extensive benefits for the environment, economy, and society at large. Addressing the urgent challenges of climate change and resource depletion necessitates prioritizing energy efficiency in transportation to construct a more resilient and equitable future. This research delves into the development of bio-inspired neural dynamics, nature-inspired swarm intelligence, fuzzy logic, heuristic algorithms, and optimization techniques for optimal control and navigation of electrified and unmanned ground vehicles. Drawing inspiration from biological systems, this research aims to enhance the performance of robots in dynamic and unstructured environments. The approach encompasses a hybrid bio-inspired method, leveraging the mathematical model of a biological neural system's membrane to facilitate smooth trajectory tracking and bounded velocities for a differential drive robot. Additionally, integration of a Leader-Slime Mold Algorithm (L-SMA) for global path optimization and a modified velocity obstacle (MVO) for local motion planning is pursued. A heuristic algorithm is also devised to enhance decision-making in uncertain and dynamic environments by coordinating actions among the L-SMA path planner, the MVO local motion planner, and the enhanced bio-inspired tracking controller. Furthermore, a real-time optimal predictive controller is proposed to address the energy management challenges of electrified vehicles while improving driveability and comfort. This predictive controller employs a linear parameter-varying model of an electrified vehicle, a custom-designed adaptive cost function, and fuzzy logic to adapt a subset of cost function weights. The integration of fuzzy logic and the adaptive predictive controller yields a convex optimization problem solved in real-time using an active-set solver. To further enhance the energy efficiency of the electrified vehicle, a particle swarm optimization enhanced model predictive controller is suggested as an adaptive cruise controller with superior energy efficiency and safety in vehicle-following scenarios. Through these integrated approaches, the aim is to advance the capabilities of autonomous robotics and electrified transportation systems, thereby contributing to safer, more efficient, and sustainable mobility solutions.

Asset managers continue to prepare physical infrastructure investments needed to accommodate the emerging technologies, namely vehicle connectivity, electrification, and automation. The provision of new infrastructure and modification of existing infrastructure is expected to incur a significant amount of capital investment. Secondly, with increasing EV and CAV operations, the revenues typically earned from vehicle registrations and fuel tax are expected to change due to changing demand for vehicle ownership and amount of travel, respectively. This research estimated (i) the changes in highway expenditures in an era of ECAV operations, (ii) the net change in highway revenues that can be expected to arise from ECAV operations, and (iii) the changes in user equity across the highway user groups (vehicle classes). In assessing the changes in highway expenditures, the research developed a model to predict the cost of highway infrastructure stewardship based on current and/ or future system usage. <div><br></div><div>The results of the research reveal that CAVs are expected to significantly change the travel patterns, leading to increased system usage which in turn results in increased wear and tear on highway infrastructure. This, with the need for new infrastructure to support and accommodate the new technologies is expected to result in increased highway expenditure. At the same time, CAVs are expected to have significantly improved fuel economy as compared to their human driven counterparts, leading to a decrease in fuel consumption per vehicle, resulting in reduced fuel revenues. Furthermore, the prominence of EVs is expected to exacerbate this problem. This thesis proposed a revision to the current user fee structure to address these impacts. This revision contains two major parts designed to address the system efficiency and equity in the near and long term. For the near term, this thesis recommended a variable tax scheme under which each vehicle class pays a different fuel tax rate. This ensures that both equity and system efficiency are improved during the transition to ECAV. In the long term, this thesis recommended supplementing the fuel tax with a distance based VMT tax, applicable to electric vehicles.<br></div>

No description available.

<p dir="ltr">The subject of this dissertation is the development of Machine Learning (ML) algorithms and their applications in Connected and Automated Vehicles (CAV). Decentralized machine learning algorithms have been proposed for CAV with noisy communication channels. Decentralized ML algorithms, referred to as Federated Learning (FL), are developed for multiple vehicles to collaboratively train models, thus enhancing performance while ensuring data privacy and security. Applications of FL for CAV (FL4CAV) are analyzed. Both centralized and decentralized FL frameworks are considered, along with various data sources, models, and data security techniques relevant to FL in CAVs. Three innovative algorithms for Decentralized Federated Learning (DFL) that effectively handle noisy communication channels are proposed. Theoretical and experimental results demonstrate that the proposed algorithms that share gradients through noisy channels instead of parameters are more robust under noisy conditions compared to parameter-mixing algorithms. Building on the exploration of decentralized federated learning, a novel decentralized noisy model update tracking algorithm is proposed to further enhance robustness and efficiency while addressing the challenge of data heterogeneity impact. The proposed algorithm performs better than the existing algorithms in handling imperfect information sharing. Expanding on these findings, applications of FL are proposed for heavy-duty diesel engines, which remain crucial due to their fuel efficiency and emissions characteristics. Finally, an FL algorithm to better predict and control aftertreatment temperature overshoots in real-time is demonstrated. </p>

Public transport prioritization is used at signalized intersections to reduce travel times and increase the attractiveness of public transport. In the future, analog communication technologies for public transport prioritization are soon to be replaced by the promising vehicle-to-everything (V2X) technology. This abstract presents a holistic approach using V2X communication in public transport prioritization for an automated vehicle. In order to take full advantage of the V2X technology, this means to V2X-enable the traffic infrastructure and change the way of communication as well as the traffic light control. The approach was implemented and tested under real-life conditions at the research intersection Tostmannplatz in Braunschweig.

<p>  </p> <p>Advancements in technology have propelled the availability of enriched and more frequent information about traffic conditions as well as the external factors that impact traffic such as weather, emergency response etc. Most newer vehicles are equipped with sensors that transmit their data back to the original equipment manufacturer (OEM) at near real-time fidelity. A growing number of such connected vehicles (CV) and the advent of third-party data collectors from various OEMs have made big data for traffic commercially available for use. Agencies maintaining and managing surface transportation are presented with opportunities to leverage such big data for efficiency gains. The focus of this dissertation is enhancing the use of CV data and applications derived from fusing it with other datasets to extract meaningful information that will aid agencies in data driven efficient decision making to improve network wide mobility and safety performance.   </p> <p>One of the primary concerns of CV data for agencies is data sampling, particularly during low-volume overnight hours. An evaluation of over 3 billion CV records in May 2022 in Indiana has shown an overall CV penetration rate of 6.3% on interstates and 5.3% on non-interstate roadways. Fusion of CV traffic speeds with precipitation intensity from NOAA’s High-Resolution Rapid-Refresh (HRRR) data over 42 unique rainy days has shown reduction in the average traffic speed by approximately 8.4% during conditions classified as very heavy rain compared to no rain. </p> <p>Both aggregate analysis and disaggregate analysis performed during this study enables agencies and automobile manufacturers to effectively answer the often-asked question of what rain intensity it takes to begin impacting traffic speeds. Proactive measures such as providing advance warnings that improve the situational awareness of motorists and enhance roadway safety should be considered during very heavy rain periods, wind events, and low daylight conditions.</p> <p>Scalable methodologies that can be used to systematically analyze hard braking and speed data were also developed. This study demonstrated both quantitatively and qualitatively how CV data provides an opportunity for near real-time assessment of work zone operations using metrics such as congestion, location-based speed profiles and hard braking. The availability of data across different states and ease of scalability makes the methodology implementable on a state or national basis for tracking any highway work zone with little to no infrastructure investment. These techniques can provide a nationwide opportunity in assessing the current guidelines and giving feedback in updating the design procedures to improve the consistency and safety of construction work zones on a national level.  </p> <p>CV data was also used to evaluate the impact of queue warning trucks sending digital alerts. Hard-braking events were found to decrease by approximately 80% when queue warning trucks were used to alert motorists of impending queues analyzed from 370 hours of queueing with queue trucks present and 58 hours of queueing without the queue trucks present, thus improving work zone safety. </p> <p>Emerging opportunities to identify and measure traffic shock waves and their forming or recovery speed anywhere across a roadway network are provided due to the ubiquity of the CV data providers. A methodology for identifying different shock waves was presented, and among the various case studies found typical backward forming shock wave speeds ranged from 1.75 to 11.76 mph whereas the backward recovery shock wave speeds were between 5.78 to 16.54 mph. The significance of this is illustrated with a case study of  a secondary crash that suggested  accelerating the clearance by 9 minutes could have prevented the secondary crash incident occurring at the back of the queue. Such capability of identifying and measuring shock wave speeds can be utilized by various stakeholders for traffic management decision-making that provide a holistic perspective on the importance of both on scene risk as well as the risk at the back of the queue. Near real-time estimation of shock waves using CV data can recommend travel time prediction models and serve as input variables to navigation systems to identify alternate route choice opportunities ahead of a driver’s time of arrival.   </p> <p>The overall contribution of this thesis is developing scalable methodologies and evaluation techniques to extract valuable information from CV data that aids agencies in operational decision making.</p>