Matching Items (3)
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- All Subjects: Automotive Engineering
- Creators: Yang, Yezhou
Description
Intelligent transportation systems (ITS) are a boon to modern-day road infrastructure. It supports traffic monitoring, road safety improvement, congestion reduction, and other traffic management tasks. For an ITS, roadside perception capability with cameras, LIDAR, and RADAR sensors is the key. Among various roadside perception technologies, vehicle keypoint detection is a fundamental problem, which involves detecting and localizing specific points on a vehicle, such as the headlights, wheels, taillights, etc. These keypoints can be used to track the movement of the vehicles and their orientation. However, there are several challenges in vehicle keypoint detection, such as the variation in vehicle models and shapes, the presence of occlusion in traffic scenarios, the influence of weather and changing lighting conditions, etc. More importantly, existing traffic perception datasets for keypoint detection are mainly limited to the frontal view with sensors mounted on the ego vehicles. These datasets are not designed for traffic monitoring cameras that are mounted on roadside poles. There’s a huge advantage of capturing the data from roadside cameras as they can cover a much larger distance with a wider field of view in many different traffic scenes, but such a dataset is usually expensive to construct. In this research, I present SKOPE3D: Synthetic Keypoint Perception 3D dataset, a one-of-its-kind synthetic perception dataset generated using a simulator from the roadside perspective. It comes with 2D bounding boxes, 3D bounding boxes, tracking IDs, and 33 keypoints for each vehicle in the scene. The dataset consists of 25K frames spanning over 28 scenes with over 150K vehicles and 4.9M keypoints. A baseline keypoint RCNN model is trained on the dataset and is thoroughly evaluated on the test set. The experiments show the capability of the synthetic dataset and knowledge transferability between synthetic and real-world data.
ContributorsPahadia, Himanshu (Author) / Yang, Yezhou (Thesis advisor) / Lu, Duo (Committee member) / Farhadi Bajestani, Mohammad (Committee member) / Arizona State University (Publisher)
Created2023
Description
To date, there is not a standardized method for consistently quantifying the performance of an automated driving system (ADS)-equipped vehicle (AV). The purpose of this dissertation is to contribute to a framework for such an approach referred to throughout as the operational safety assessment (OSA) methodology. Through this research, safety metrics are identified, researched, and analyzed to capture aspects of the operational safety of AVs, interacting with other salient objects. This dissertation outlines the approach for developing this methodology through a series of key steps including: (1) comprehensive literature review; (2) research and refinement of OSA metrics; (3) generation of MATLAB script for metric calculations; (4) generation of simulated events for analysis; (5) collection of real-world data for analysis; (6) review of OSA methodology results; and (7) discussion of future work to expand complexity, fidelity, and relevance aspects of the OSA methodology. The detailed literature review includes the identification of metrics historically used in both traditional and more recent evaluations of vehicle performance. Subsequently, the metric formulations are refined, and robust severity evaluations are proposed. A MATLAB script is then presented which was generated to calculate the metrics from any given source assuming proper formatting of the data. To further refine the formulations and the MATLAB script, a variety of simulated scenarios are discussed including car-following, intersection, and lane change situations. Additionally, a data collection activity is presented, leveraging the SMARTDRIVE testbed operated by Maricopa County Department of Transportation in Anthem, AZ to collect real-world data from an active intersection. Lastly, the efficacy of the OSA methodology with respect to the evaluation of vehicle performance for a set of scenarios is evaluated utilizing both simulated and real-world data. This assessment provides a demonstration of the ability and robustness of this methodology to evaluate vehicle performance for a given scenario. At the conclusion of this dissertation, additional factors including fidelity, complexity, and relevance are explored to contribute to a more comprehensive evaluation.
ContributorsComo, Steven Gerard (Author) / Wishart, Jeffrey (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Chen, Yan (Committee member) / Favaro, Francesca (Committee member) / Arizona State University (Publisher)
Created2022
Description
The need for robust verification and validation of automated vehicles (AVs) to ensure driving safety grows more urgent as increasing numbers of AVs are allowed to operate on open roads. To address this need, AV developers can present a safety case to regulators and the public that provides an evidence-based justification of their assertion that an AV is safe to operate on open roads. This work aims to describe the development of a scenario-based testing methodology that contributes to this safety case. A high-level definition of this test selection and scoring methodology (TSSM) is first presented, along with an outline of its scope and key ideas. This is followed by a literature review that details the current state of the art in AV testing, including the driving performance metrics and equations that provide a basis for the TSSM. A chart-based method for quantifying an AV’s operational design domain (ODD) and behavioral competency portfolio is then described that provides the foundation for a scenario generation and filtration process. After outlining a method for the AV to progress through increasingly robust test methods based on its current technology readiness level (TRL), the generation and filtration of two sets of scenarios by the TSSM is outlined: a standardized set that can be used to compare the performance of vehicles with identical ODD and behavioral competency portfolios, and a set containing high-relevance scenarios that is partially randomized to ensure test integrity. A related framework for incorporating testing on open roads is subsequently specified. An equation for an overall AV driving performance score is then defined that quantifies the aggregate performance of the AV across all generated scenarios. The TSSM continues according to an iterative process, which includes a method for exploring edge and corner scenarios, until a stopping condition is achieved. Two proofs of concept are provided: a demonstration of the ability of the TSSM to pare scenarios from a preexisting database, and an example ODD and behavioral competency portfolio specification form. Finally, this work concludes by evaluating the TSSM and its proofs of concept and outlining possible future work on the methodology.
ContributorsO'Malley, Gavin (Author) / Wishart, Jeffrey (Thesis advisor) / Zhao, Junfeng (Thesis advisor) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023