Indiana University-Purdue University Indianapolis (IUPUI) / The current technologies employed for autonomous driving provide tremendous performance and results, but the technology itself is far from mature and relatively expensive. Some of the most commonly used components for autonomous driving include LiDAR, cameras, radar, and ultrasonic sensors. Sensors like such are usually high-priced and often require a tremendous amount of computational power in order to process the gathered data. Many car manufacturers consider cameras to be a low-cost alternative to some other costly sensors, but camera based sensors alone are prone to fatal perception errors. In many cases, adverse weather and night-time conditions hinder the performance of some vision based sensors. In order for a sensor to be a reliable source of data, the difference between actual data values and measured or perceived values should be as low as possible. Lowering the number of sensors used provides more economic freedom to invest in the reliability of the components used. This thesis provides an alternative approach to the current autonomous driving methodologies by utilizing acoustic signatures of moving objects. This approach makes use of a microphone array to collect and process acoustic signatures captured for simultaneous localization and mapping (SLAM). Rather than using numerous sensors to gather information about the surroundings that are beyond the reach of the user, this method investigates the benefits of considering the sound waves of different objects around the host vehicle for SLAM. The components used in this model are cost-efficient and generate data that is easy to process without requiring high processing power. The results prove that there are benefits in pursuing this approach in terms of cost efficiency and low computational power. The functionality of the model is demonstrated using MATLAB for data collection and testing.

With the advancement of autonomous driving research, 3D detection based on LiDAR point cloud has gradually become one of the top research topics in the field of artificial intelligence. Compared with RGB cameras, LiDAR point cloud can provide depth information, while RGB images can provide denser resolution. Features from LiDAR and cameras are considered to be complementary. However, due to the sparsity of the LiDAR point clouds, a dense and accurate RGB/3D projective relationship is difficult to establish especially for distant scene points. Recent works try to solve this problem by designing a network that learns missing points or dense point density distribution to compensate for the sparsity of the LiDAR point cloud. During the master’s exploration, we consider addressing this problem from two aspects. The first is to design a GAN(Generative Adversarial Network)-based module to reconstruct point clouds, and the second is to apply regional point cloud enhancement based on motion maps. In the first aspect, we propose to use an imagine-and-locate process, called UYI. The objective of this module is to improve the point cloud quality and is independent of the detection stage used for inference. We accomplish this task through a GAN-based cross-modality module that uses image as input to infer a dense LiDAR shape. In another aspect, inspired by the attention mechanism of human eyes, we use motion maps to perform random augmentation on point clouds in a targeted manner named motion map-assisted enhancement, MAE. Boosted by our UYI and MAE module, our experiments show a significant performance improvement in all tested baseline models. In fact, benefiting from the plug-and-play characteristics of our module, we were able to push the performance of the existing state-of-the-art model to a new height. Our method not only has made great progress in the detection performance of vehicle objects but also achieved an even bigger leap forward in the pedestrian category. In future research, we will continue to explore the feasibility of spatio-temporal correlation methods in 3D detection, and 3D detection related to motion information extraction could be a promising direction.

<p dir="ltr">Autonomous driving technology has rapidly advanced in recent years, leading to significant developments in its deployment and application. This paper presents the design and deployment of a real-world autonomous driving test platform with comprehensive capabilities, enabling the test and evaluation of autonomous driving technologies in real-world scenarios. The platform integrates multiple sensors, including LiDAR, radar, cameras, Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU), which collectively provide robust sensing, localization, and measurement capabilities. Built on the Autoware.AI framework, this test platform offers a flexible environment for diverse autonomous driving functionalities, including mapping, object detection, planning, and control. The use of ROS (Robot Operating System) enables seamless communication between various system modules, simplifies sensor integration, and provides extensive tools for debugging and visualization, making the platform highly adaptable for both research development and algorithm validation.</p><p dir="ltr">As a key demonstration of the platform’s capabilities, the paper introduces Talk2Drive, a Large Language Model (LLM)-based autonomous driving framework designed to enhance human-vehicle interaction. Talk2Drive leverages advanced AI techniques to interpret and execute verbal commands from the driver, enabling real-time adjustments to vehicle behavior and offering a personalized driving experience. This paper indicates the comprehensive integration and deployment process of the Talk2Drive framework. Additionally, through various experimental setups—including highway driving, intersections, and parking lot scenarios—the paper demonstrates how this autonomous driving platform evaluates the safety, performance, adaptability, and reliability of AI-driven frameworks like Talk2Drive under the real-world condition. The results underscore the platform's effectiveness in testing and validating autonomous systems. At the same time, the successful deployment of Talk2Drive also proves that the designed autonomous driving testing platform has the capabilities to examine the complex autonomous driving algorithm or systems' performance in real-world environments.</p>

Traffic congestion and safety has become a major issue in the modern world's commute. Congestion has been causing people to travel billions of hours more and to purchase billions of gallons of fuel extra which account to congestion cost of billions of dollars. Autonomous driving vehicles have been one solution to this problem because of their huge impact on efficiency, pollution, and human safety. Also, extensive research has been carried out on control design of vehicular platoons because a further improvement in traffic throughput while not compromising the safety is possible when the vehicles in the platoon are provided with better predictive abilities. Motion control is a key area of autonomous driving research that handles moving parts of vehicles in a deliberate and controlled manner. A widely worked on problem in motion control concerned with time parameterized reference tracking is trajectory tracking. Having an efficient and effective tracking algorithm embedded in the autonomous driving system is the key for better performance in terms of resources consumed and tracking error. Many tracking control algorithms in literature rely on an accurate model of the vehicle and often, it can be an intimidating task to come up with an accurate model taking into consideration various conditions like friction, heat effects, ageing processes etc. And typically, control algorithms rely on periodic execution of the tasks that update the control actions, but such updates might not be required, which result in unnecessary actions that waste resources. The main focus of this work is to design an intermittent model-free optimal control algorithm in order to enable autonomous vehicles to track trajectories at high-speeds. To obtain a solution which is model-free, a Q-learning setup with an actor-network to approximate the optimal intermittent controller and a critic network to approximate the optimal cost, resulting in the appropriate tuning laws is considered. / Master of Science / A risen research effort in the area of autonomous vehicles has been witnessed in the past few decades because these systems improve safety, comfort, transport time and energy consumption which are some of the main issues humans are facing in the modern world’s highway systems. Systems like emergency braking, automatic parking, blind angle vehicle detection are creating a safer driving environment in populated areas. Advanced driver assistance systems (ADAS) are what such kind of systems are known as. An extension of these partially automated ADAS are vehicles with fully automated driving abilities, which are able to drive by themselves without any human involvement. An extensively proposed approach for making traffic throughput more efficient on existing highways is to assemble autonomous vehicles into platoons. Small intervehicle spacing and many vehicles constituting each platoon formation improve the traffic throughput significantly. Lately, the advancements in computational capabilities, in terms of both algorithms and hardware, communications, and navigation and sensing devices contributed a lot to the development of autonomous systems (both single and multiagent) that operate with high reliability in uncertain/dynamic operating conditions and environments. Motion control is an important area in the autonomous vehicles research. Trajectory-tracking is a widely studied motion control scenario which is about designing control laws that force a system to follow some time-dependent reference path and it is important to have an effective and efficient trajectory-tracking control law in an autonomous vehicle to reduce the resources consumed and tracking error. The goal of this work is to design an intermittent model-free trajectory tracking control algorithm where there is no need of any mathematical model of the vehicle system being controlled and which can reduce the controller updates by allowing the system to evolve in an open loop fashion and close the loop only when an user defined triggering condition is satisfied. The approach is energy efficient in that the control updates are limited to instances when they are needed rather than unnecessary periodic updates. Q-learning which is a model-free reinforcement learning technique is used in the trajectory tracking motion control algorithm to make the vehicles track their respective reference trajectories without any requirement of their motion model, the knowledge of which is generally needed when dealing with a motion control problem. The testing of the designed algorithm in simulations and experiments is presented in this work. The study and development of a vehicle platform in order to perform the experiments is also discussed. Different motion control and sensing techniques are presented and used. The vehicle platform is shown to track a reference trajectory autonomously without any human intervention, both in simulations and experiments, proving the effectiveness of the proposed algorithm.

<p dir="ltr">E-commerce, fueled by the digital revolution, has become a cornerstone of modern retail, driving demand for efficient last-mile logistics services. As online sales soar past $4 trillion, the need for streamlined, cost-effective delivery solutions is urgent, particularly in markets like China, where complex traffic conditions and high customer expectations complicate last-mile delivery. Autonomous driving technology offers a promising approach to meeting these challenges, enabling lower costs and improved delivery efficiency. However, cities such as Suzhou present unique obstacles for autonomous delivery vehicles (ADVs), with unpredictable traffic and diverse obstacles like pedestrians and bicycles. To tackle these issues, this research developed a high-capacity simulation platform capable of executing 300,000 scenarios weekly. It incorporates advanced routing algorithms, such as the Shortest Path Faster Algorithm (SPFA), and high-definition mapping (HDMap) for precise localization, supporting rigorous testing across varied urban logistics scenarios. The platform’s modular microservices architecture ensures scalability, enabling thorough validation of both software and hardware components in unmanned logistics vehicles. Findings demonstrate that the platform’s architecture, particularly its modular microservices and Protocol Buffers for data handling, optimizes the reliability and safety of autonomous systems in dense urban environments. Realistic scenario generation through SPFA routing and HDMap integration provides a robust environment for decision-making tests, contributing to enhanced operational stability and efficiency.</p><p dir="ltr">Practical Implications extend beyond autonomous driving, suggesting relevance to intelligent transportation systems, delivery drones, and smart cities. The platform’s high-throughput capacity underscores the importance of large-scale testing, enabling rapid development cycles with minimal dependence on real-world testing. This research provides a foundation for future improvements in simulation efficiency, scenario diversity, and applications across various sectors, paving the way for further advancements in autonomous technology.</p>

2024 / Autonomous driving research faces significant challenges in transitioning from simulation-based evaluations to real-world implementations. While simulation environments offer controlled settings for training driving agents, real-world scenarios introduce unforeseen complexities crucial for assessing the robustness and adaptability of these agents. This study addresses two pivotal questions in autonomous driving research: (1) the translation of simulated experiences to a real-world environment, and (2) the correlation between offline evaluation metrics and closed-loop driving performance To address the first question, we employ a novel method using pre-trained foundation models to abstract vision input. This allows us to train driving policies in simulation and assess their performance with real-world data, investigating the effectiveness of Sim2Real for driving scenarios. For the second question, we analyze the relationship between a selected set of offline metrics and established closed-loop metrics in both simulation and real-world contexts. By comparing their performance, we aim to ascertain the efficacy of offline evaluations in predicting closed-loop driving behavior. Our research aims to bridge the gap between simulation and real-world environments, understanding the efficacy of open-loop evaluation in autonomous driving.

<p dir="ltr">The growth of electric vehicles (EVs) offers several benefits for air quality improvement and emissions reduction. Nonetheless, EVs also pose several challenges in the area of highway transportation. These barriers are related to the limitations of EV technology, particularly the charge duration and speed of battery recharging, which translate to vehicle range anxiety for EV users. A promising solution to these concerns is V2V DWC technology (Vehicle to Vehicle Dynamic Wireless Charging), particularly mobile energy disseminators (MEDs). The MED is mounted on a large vehicle or truck that charges all participating EVs within a specified locus from the MED. However, current research on MEDs offers solutions that are widely considered impractical for deployment, particularly in urban environments where range anxiety is common. Acknowledging such gap in the literature, this thesis proposes a comprehensive methodological framework for optimal MED deployment decisions. In the first component of the framework, a practical system, termed “ChargingEnv” is developed using reinforcement learning (RL). ChargingEnv simulates the highway environment, which consists of streams of EVs and an MED. The simulation accounts for a possible misalignment of the charging panel and incorporates a realistic EV battery model. The second component of the framework uses multiple deep RL benchmark models that are trained in “ChargingEnv” to maximize EV service quality within limited charging resource constraints. In this study, numerical experiments were conducted to demonstrate the MED deployment decision framework’s efficacy. The findings indicate that the framework’s trained model can substantially improve EV travel range and alleviate battery depletion concerns. This could serve as a vital tool that allows public-sector road agencies or private-sector commercial entities to efficiently orchestrate MED deployments to maximize service cost-effectiveness.</p>

In recent years, autonomous driving has become an increasingly practical technology. With state-of-the-art computer and sensor engineering, autonomous vehicles may be produced and widely used for travel and logistics in the near future. They have great potential to reduce traffic accidents, improve transportation efficiency, and release people from driving tasks while commuting. Researchers have built autonomous vehicles that can drive on public roads and handle normal surrounding traffic and obstacles. However, in situations like lane changing and merging, the autonomous vehicle faces the challenge of performing smooth interaction with human-driven vehicles. To do this, autonomous vehicle intelligence still needs to be improved so that it can better understand and react to other human drivers on the road. In this thesis, we argue for the importance of implementing ”socially cooperative driving”, which is an integral part of everyday human driving, in autonomous vehicles. An intention-integrated Prediction- and Cost function-Based algorithm (iPCB) framework is proposed to enable an autonomous vehicles to perform cooperative social behaviors. We also propose a behavioral planning framework to enable the socially cooperative behaviors with the iPCB algorithm. The new architecture is implemented in an autonomous vehicle and can coordinate the existing Adaptive Cruise Control (ACC) and Lane Centering interface to perform socially cooperative behaviors. The algorithm has been tested in over 500 entrance ramp and lane change scenarios on public roads in multiple cities in the US and over 10; 000 in simulated case and statistical testing. Results show that the proposed algorithm and framework for autonomous vehicle improves the performance of autonomous lane change and entrance ramp handling. Compared with rule-based algorithms that were previously developed on an autonomous vehicle for these scenarios, over 95% of potentially unsafe situations are avoided.

Vehicle-to-Vehicle communication is the key technology for achieving increased perception for automated vehicles where the communication allows virtual sensing with the use of sensors placed in other vehicles. In addition, this technology also allows recognising objects that are out-of-sight. This thesis presents a Trust System that allows a vehicle to make more reliable and robust decisions. The system evaluates the current situation and generates a Trust Index indicating the level of trust in the environment, the ego vehicle, and the other vehicles. Current research focuses on securing the communication between the vehicles themselves, but does not verify the content of the received data on a system level. The proposed Trust System evaluates the received data according to sensor accuracy, behaviour of other vehicles, and the perception of the local environment. The results show that the proposed method is capable of correctly identifying various situations and discusses how the Trust Index can be used to make more robust decisions.

Trajectory planning is an important component of autonomous driving. It takes the result of route-level navigation plan and generates the motion-level commands that steer an autonomous passenger vehicle (APV). Prior work on solving this problem uses either a sampling-based or optimization-based trajectory planner, accompanied by some high-level rule generation components.