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Cited by 317Autonomous Vehicle Technology and SafetyHuman-Automation Interaction and SafetyTraffic and Road Safety

Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety

Open access
Trivedi, Mohan Manubhai, Gandhi, Tarak, McCall, Joel·2007·IEEE Transactions on Intelligent Transportation Systems
DOI 10.1109/tits.2006.889442
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Abstract

This paper presents investigations into the role of computer-vision technology in developing safer automobiles. We consider vision systems, which cannot only look out of the vehicle to detect and track roads and avoid hitting obstacles or pedestrians but simultaneously look inside the vehicle to monitor the attentiveness of the driver and even predict her intentions. In this paper, a systems-oriented framework for developing computer-vision technology for safer automobiles is presented. We will consider three main components of the system: environment, vehicle, and driver. We will discuss various issues and ideas for developing models for these main components as well as activities associated with the complex task of safe driving. This paper includes a discussion of novel sensory systems and algorithms for capturing not only the dynamic surround information of the vehicle but also the state, intent, and activity patterns of drivers

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  • Previous works have addressed maneuver anticipation through sensory-fusion from multiple cameras, GPS, and vehicle dynamics.

    “Previous works have addressed maneuver anticipation [1, 19, 27, 9, 37] through sensory-fusion from multiple cameras, GPS, and vehicle dynamics.”

    From Recurrent Neural Networks for driver activity anticipation via sensory-fusion architecture (2016) · cites this paper as [37]

    The Lane-Change-Intent Analysis section describes predicting a lane change by combining visual information from inside and outside the vehicle with vehicle speed, yaw rate, and acceleration. The LISA-Q section lists multiple cameras, GPS, and vehicle-state sensors, but the described lane-change-intent classifier does not use GPS. Thus the paper supports maneuver anticipation through camera and vehicle-dynamics fusion, not the full sensor combination claimed. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Morris et al. and Trivedi et al. used a Relevance Vector Machine for intent prediction and performed sensory fusion by concatenating feature vectors.

    “In particular, Morris et al. [27] and Trivedi et al. [37] used a Relevance Vector Machine (RVM) for intent prediction and performed sensory fusion by concatenating feature vectors.”

    From Recurrent Neural Networks for driver activity anticipation via sensory-fusion architecture (2016) · cites this paper as [37]

    The Lane-Change-Intent Analysis section describes predicting lane-change intent with a sparse-Bayesian-learning classifier that combines features from lane tracking, driver head motion, and vehicle parameters. It does not identify the classifier as a Relevance Vector Machine or say that fusion was performed by concatenating feature vectors. Page numbers are unavailable in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 32

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Cited by

Sorted by citations

Authors
Trivedi, M. M., Gandhi, T., & McCall, J.
Affiliations
  • University of California San Diego (US)
  • Microsoft (United States) (US)
ORCID
Trivedi, Mohan Manubhai
Venue
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2007-03
Volume
8
Issue
1
Pages
108-120
ISSN
1524-9050 (ISSN-L)
DOI
10.1109/tits.2006.889442
OpenAlex
W2162508706
Cited by
317
References
1
Citations by year
2026: 4 · 2025: 9 · 2024: 10 · 2023: 9 · 2022: 7 · 2021: 13 · 2020: 10 · 2019: 14 · 2018: 18 · 2017: 17 · 2016: 17 · 2015: 25 · 2014: 26 · 2013: 24 · 2012: 20
FWCI
53.17
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • University of California, San Diego
  • Information Technology Laboratory
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
Keywords
computer vision, vehicle safety, driver monitoring, driver attention, driver intention prediction, pedestrian detection, obstacle avoidance, road detection

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