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BigRoad: Scaling Road Data Acquisition for Dependable Self-Driving

Authors:

Cagdas Karatas,

Marco Gruteser,

Richard P. MartinAuthors Info & Claims

MobiSys '17: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services

Pages 371 - 384

https://doi.org/10.1145/3081333.3081344

Published: 16 June 2017 Publication History

Abstract

Advanced driver assistance systems and, in particular automated driving offers an unprecedented opportunity to transform the safety, efficiency, and comfort of road travel. Developing such safety technologies requires an understanding of not just common highway and city traffic situations but also a plethora of widely different unusual events (e.g., object on the road way and pedestrian crossing highway, etc.). While each such event may be rare, in aggregate they represent a significant risk that technology must address to develop truly dependable automated driving and traffic safety technologies. By developing technology to scale road data acquisition to a large number of vehicles, this paper introduces a low-cost yet reliable solution, BigRoad, that can derive internal driver inputs (i.e., steering wheel angles, driving speed and acceleration) and external perceptions of road environments (i.e., road conditions and front-view video) using a smartphone and an IMU mounted in a vehicle. We evaluate the accuracy of collected internal and external data using over 140 real-driving trips collected in a 3-month time period. Results show that BigRoad can accurately estimate the steering wheel angle with 0.69 degree median error, and derive the vehicle speed with 0.65 km/h deviation. The system is also able to determine binary road conditions with 95% accuracy by capturing a small number of brakes. We further validate the usability of BigRoad by pushing the collected video feed and steering wheel angle to a deep neural network steering wheel angle predictor, showing the potential of massive data acquisition for training self-driving system using BigRoad.

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Cited By

Guo AGao XChen ZXiao YLiu JGe XSun WFang CChristakis MPradel M(2024)CooTest: An Automated Testing Approach for V2X Communication SystemsProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680373(1453-1465)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680373
Sutradhar KVenkatesh R(2024)SVQCP: A Secure Vehicular Quantum Communication ProtocolIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.339615711:5(4850-4859)Online publication date: Sep-2024
https://doi.org/10.1109/TNSE.2024.3396157
Papatheocharous EKaiser CMoser JStocker A(2023)Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature ReviewSensors10.3390/s2317750523:17(7505)Online publication date: 29-Aug-2023
https://doi.org/10.3390/s23177505
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Index Terms

BigRoad: Scaling Road Data Acquisition for Dependable Self-Driving
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing
    2. Other architectures
      1. Neural networks
  2. Real-time systems
    1. Real-time system architecture
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing
    2. Ubiquitous and mobile devices
      1. Mobile phones

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cover image ACM Conferences

MobiSys '17: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services

June 2017

520 pages

ISBN:9781450349284

DOI:10.1145/3081333

General Chairs:
Tanzeem Choudhury
Cornell University, USA
,
Steve Ko
University at Buffalo, USA
,
Program Chairs:
Andrew Campbell
Dartmouth College, USA
,
Deepak Ganesan
University of Massachusetts, USA

Copyright © 2017 ACM.

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Published: 16 June 2017

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National Science Foundation

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MobiSys'17

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SIGMOBILE

MobiSys'17: The 15th Annual International Conference on Mobile Systems, Applications, and Services

June 19 - 23, 2017

New York, Niagara Falls, USA

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MobiSys '17 Paper Acceptance Rate 34 of 188 submissions, 18%;

Overall Acceptance Rate 274 of 1,679 submissions, 16%

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Cited By

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https://dl.acm.org/doi/10.1145/3650212.3680373
Sutradhar KVenkatesh R(2024)SVQCP: A Secure Vehicular Quantum Communication ProtocolIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.339615711:5(4850-4859)Online publication date: Sep-2024
https://doi.org/10.1109/TNSE.2024.3396157
Papatheocharous EKaiser CMoser JStocker A(2023)Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature ReviewSensors10.3390/s2317750523:17(7505)Online publication date: 29-Aug-2023
https://doi.org/10.3390/s23177505
Abdallah RWu BWan J(2023)Real-Time Vehicle Localization Using Steering Wheel Angle in Urban Cities2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)10.1109/MOST57249.2023.00015(62-70)Online publication date: May-2023
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