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- Title
- Computationally Efficient Predictive Control Strategies for Autonomous Vehicles
- Creator
- Bhattacharyya, Viranjan
- Date
- 2021
- Description
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This thesis aims at developing computationally efficient (hence real-time applicable) control strategies for autonomous vehicles in the...
Show moreThis thesis aims at developing computationally efficient (hence real-time applicable) control strategies for autonomous vehicles in the presence of uncertainty, while incorporating high fidelity vehicle dynamics. The motivation for the control strategies is to ensure safety and improve energy efficiency of the vehicles. In this research, an effort has been made to develop control strategies to strike a balance between these competing factors. The specific contributions are: development of a new hierarchical control framework that can guarantee avoidance of red-light idling in the presence of uncertainty in preceding vehicle information/prediction in connected environment (hence improves system mobility); exploitation of a data-driven modeling approach for identifying a linear predictor for the nonlinear vehicle dynamics, which facilitates formulation of a convex equivalent problem of the original non-convex problem (hence facilitates computational tractability); introduction of a novel vehicle dynamics-aware fast game-theoretic planner for behavior and motion planning of vehicles in uncertain and unconnected environments. This thesis explores both the possible directions of future autonomous vehicles: connected and unconnected autonomous vehicles. In particular, the first problem relates to longitudinal fuel efficient driving (eco-driving) in a connected urban environment, where the connected and automated vehicles (CAVs) aim at the improvement of fuel efficiency and reduction of red-light idling (stop and go motion). The CAVs also focus on ensuring collision avoidance with the preceding vehicles despite the prediction uncertainty in future trajectory of preceding vehicles. This problem assumes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, and is a longitudinal control problem. The next problem considers the uncertainty in prediction of future states of neighbouring vehicles in an unconnected environment and involves both lateral and longitudinal control. Following previous research, the interactive nature of driving is modeled using game-theory and a computationally efficient game-theoretic planner is introduced. Simulation results show the efficacy of the proposed methods in terms of computational tractability and fuel-efficiency.
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- Title
- Cities on the Move: Envisioning Cityscape in the Autonomous Vehicle Era
- Creator
- Lee, Hyo Eun
- Date
- 2023
- Description
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As cities have developed in response to a rapidly changing environment since the industrial revolution, a variety of circumstances and issues...
Show moreAs cities have developed in response to a rapidly changing environment since the industrial revolution, a variety of circumstances and issues have arisen. In cities, technological development and the rise of private automobiles have been related to urban sprawl, traffic congestion, environmental degradation, and mobility inequalities. Currently, autonomous vehicles are bringing a paradigm shift in mobility as technological innovations accompany the fourth industrial revolution. As cities undergo renewal through new mobility, recent developments in driverless technologies should be considered for their long-term impact on the built environment. The purpose of this thesis is to propose a design, along with a set of scenarios, for a transportation hub in the era of autonomous vehicles to address today's complex mobility requirements. Through bringing together existing case studies, research on autonomous vehicles, and drawing conclusions regarding the relationship between mobility and urban landscapes, this thesis explores how autonomous vehicles will affect the future built environment in a set of timelines. Considering the future transportation hub will require programs and infrastructure to support autonomous vehicles, this thesis also provides a vision of the future of transportation modes and how they interact. Finally, a transportation hub design model and an automation city model for the AV era will be proposed.
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- Title
- Improving Localization Safety for Landmark-Based LiDAR Localization System
- Creator
- Chen, Yihe
- Date
- 2024
- Description
-
Autonomous ground robots have gained traction in various commercial applications, with established safety protocols covering subsystem...
Show moreAutonomous ground robots have gained traction in various commercial applications, with established safety protocols covering subsystem reliability, control algorithm stability, path planning, and localization. This thesis specifically delves into the localizer, a critical component responsible for determining the vehicle’s state (e.g., position and orientation), assessing compliance with localization safety requirements, and proposing methods for enhancing localization safety.Within the robotics domain, diverse localizers are utilized, such as scan-matching techniques like normal distribution transformations (NDT), the iterative closest point (ICP) algorithm,probabilistic maps method, and semantic map-based localization.Notably, NDT stands out as a widely adopted standalone laser localization method, prevalent in autonomous driving software such as Autoware and Apollo platforms.In addition to the mentioned localizers, common state estimators include variants of Kalman Filter, particle filter-based, and factor graph-based estimators. The evaluation of localization performance typically involves quantifying the estimated state variance for these state estimators.While various localizer options exist, this study focuses on those utilizing extended Kalman filters and factor graph methods. Unlike methods like NDT and ICP algorithms, extended Kalman filters and factor graph based approaches guarantee bounding of estimated state uncertainty and have been extensively researched for integrity monitoring.Common variance analysis, employed for sensor readings and state estimators, has limitations, primarily focusing on non-faulted scenarios under nominal conditions. This approach proves impractical for real-world scenarios and falls short for safety-critical applications like autonomous vehicles (AVs).To overcome these limitations, this thesis utilizes a dedicated safety metric: integrity risk. Integrity risk assesses the reliability of a robot’s sensory readings and localization algorithm performance under both faulted and non-faulted conditions. With a proven track record in aviation, integrity risk has recently been applied to robotics applications, particularly for evaluating the safety of lidar localization.Despite the significance of improving localization integrity risk through laser landmark manipulation, this remains an under explored territory. Existing research on robot integrity risk primarily focuses on the vehicles themselves. To comprehensively understand the integrity risk of a lidar-based localization system, as addressed in this thesis, an exploration of lidar measurement faults’ modes is essential, a topic covered in this thesis.The primary contributions of this thesis include: A realistic error estimation method for state estimators in autonomous vehicles navigating using pole-shape lidar landmark maps, along with a compensatory method; A method for quantifying the risk associated with unmapped associations in urban environments, enhancing the realism of values provided by the integrity risk estimator; a novel approach to improve the localization integrity of autonomous vehicles equipped with lidar feature extractors in urban environments through minimal environmental modifications, mitigating the impact of unmapped association faults. Simulation results and experimental results are presented and discussed to illustrate the impact of each method, providing further insights into their contributions to localization safety.
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