Pub Date : 2020-02-01DOI: 10.1109/MetroCAD48866.2020.00010
Zheng Dong, Weisong Shi, G. Tong, Kecheng Yang
This paper discusses challenges in computer systems research posed by the emerging autonomous driving systems. We first identify four research areas related to autonomous driving systems: real-time and embedded systems, machine learning, edge computing, and cloud computing. Next, we sketch two fatal accidents caused by active autonomous driving, and uses them to indicate key missing capabilities from today’s systems. In light of these research areas and shortcomings, we describe a vision of digital driving circumstances for autonomous vehicles and refer to autonomous vehicles as "clients" of this digital driving circumstance. Then we propose a new research thrust: collaborative autonomous driving. Intuitively, requesting useful information from a digital driving circumstance to enable collaborative autonomous driving is quite sophisticated (e.g., collaborations may come from different types of unstable edge devices), but it also provide us various research challenges and opportunities. The paper closes with a discussion of the research necessary to develop these capabilities.
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Pub Date : 2020-02-01DOI: 10.1109/MetroCAD48866.2020.00021
Jingda Guo, Qing Yang, Song Fu, R. Boyles, Shavon Turner, Kenzie Clarke
Sharing perception data among autonomous vehicles is extremely useful to extending the line of sight and field of view of autonomous vehicles, which otherwise suffer from blind spots and occlusions. However, the security of using data from a random other car in making driving decisions is an issue. Without the ability of assessing the trustworthiness of received information, it will be too risky to use them for any purposes. On the other hand, when information is exchanged between vehicles, it provides a golden opportunity to quantitatively study a vehicle’s trust. In this paper, we propose a trustworthy information sharing framework for connected and autonomous vehicles in which vehicles measure each other’s trust using the Dirichlet-Categorical (DC) model. To increase a vehicle’s capability of assessing received data’s trust, we leverage the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) model to increase the resolution of blurry images. As a result, a vehicle is able to evaluate the trustworthiness of received data that contain distant objects. Based on the KITTI dataset, we evaluate the proposed solution and discover that vehicle’s trust assessment capability can be increased by 11 − 37%, using the ESRGAN model.
{"title":"Towards Trustworthy Perception Information Sharing on Connected and Autonomous Vehicles","authors":"Jingda Guo, Qing Yang, Song Fu, R. Boyles, Shavon Turner, Kenzie Clarke","doi":"10.1109/MetroCAD48866.2020.00021","DOIUrl":"https://doi.org/10.1109/MetroCAD48866.2020.00021","url":null,"abstract":"Sharing perception data among autonomous vehicles is extremely useful to extending the line of sight and field of view of autonomous vehicles, which otherwise suffer from blind spots and occlusions. However, the security of using data from a random other car in making driving decisions is an issue. Without the ability of assessing the trustworthiness of received information, it will be too risky to use them for any purposes. On the other hand, when information is exchanged between vehicles, it provides a golden opportunity to quantitatively study a vehicle’s trust. In this paper, we propose a trustworthy information sharing framework for connected and autonomous vehicles in which vehicles measure each other’s trust using the Dirichlet-Categorical (DC) model. To increase a vehicle’s capability of assessing received data’s trust, we leverage the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) model to increase the resolution of blurry images. As a result, a vehicle is able to evaluate the trustworthiness of received data that contain distant objects. Based on the KITTI dataset, we evaluate the proposed solution and discover that vehicle’s trust assessment capability can be increased by 11 − 37%, using the ESRGAN model.","PeriodicalId":117440,"journal":{"name":"2020 International Conference on Connected and Autonomous Driving (MetroCAD)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124048031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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