short-paper

Open access

Predicting Traffic Accidents with Event Recorder Data

Authors:

Yoshiaki Takimoto,

Takeshi Kurashima,

Shuhei Yamamoto,

Hiroyuki TodaAuthors Info & Claims

PredictGIS'19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility

Pages 11 - 14

https://doi.org/10.1145/3356995.3364535

Published: 05 November 2019 Publication History

Abstract

Large amounts of data on accidents are continually being collected by dashboard cameras (dashcams). In this paper, we address the problem of predicting the occurrence of accidents: Our goal is to predict when accidents will occur based on stored dashcam data and analysis of live video streams. We propose a survival analysis model for predicting the event occurrence time. The occurrence of accidents involves changes in the situation of own car and surroundings. Therefore, the hazard function of the proposed model is modeled by a convolutional recurrent neural network that can capture it from high-dimensional time-series information, i.e., video. Another characteristic of our model is its incorporation of location data because how likely the events are to occur strongly depends on location. Our model can predict accidents by simultaneously considering video and location data. Experiments on real-world event recorder data show that our model can more accurately predict accident occurrences than baseline models.

References

[1]

Fu-Hsiang Chan, Yu-Ting Chen, Yu Xiang, and Min Sun. 2016. Anticipating Accidents in Dashcam Videos. In ACCV. 136--153.

[2]

Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv:1406.1078

[3]

David Collett. 2003. Modelling Survival Data in Medical Research. Chapman and Hall/CRC.

[4]

Frank E Harrell, Kerry L Lee, and Daniel B Mark. 1996. Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors. Statistics in Medicine 15, 4 (1996), 361--387.

[5]

Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167

[6]

How Jing and Alexander J Smola. 2017. Neural Survival Recommender. In WSDM. ACM, 515--524.

[7]

Liangchen Luo, Yuanhao Xiong, Yan Liu, and Xu Sun. 2019. Adaptive Gradient Methods with Dynamic Bound of Learning Rate. In ICLR. New Orleans, Louisiana.

[8]

Yosihiko Ogata. 1981. On Lewis' Simulation Method for Point Processes. IEEE Trans. Inf. Theory 27, 1, 23--31.

Digital Library

[9]

Pongsathorn Raksincharoensak. 2013. Drive Recorder Database for Accident/Incident Study and Its Potential for Active Safety Development. http://web.tuat.ac.jp/~smrc/pdf/Driverecorder.pdf.

[10]

Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR abs/1409.1556 (2014).

[11]

Tomoyuki Suzuki, Hirokatsu Kataoka, Yoshimitsu Aoki, and Yutaka Satoh. 2018. Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB. arXiv:1804.02675

[12]

Jiawen Yao, Xinliang Zhu, Feiyun Zhu, and Junzhou Huang. 2017. Deep Correlational Learning for Survival Prediction from Multi-modality Data. In MICCAI. Springer, 406--414.

[13]

Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. 2017. Places: A 10 million Image Database for Scene Recognition. In TPAMI.

[14]

Xinliang Zhu, Jiawen Yao, and Junzhou Huang. 2016. Deep Convolutional Neural Network for Survival Analysis with Pathological Images. In BIBM. IEEE, 544--547.

[15]

Xinliang Zhu, Jiawen Yao, Feiyun Zhu, and Junzhou Huang. 2017. WSISA: Making Survival Prediction From Whole Slide Histopathological Images. In CVPR.

Cited By

Liao HSun HShen HWang CTian CTam KLi LXu CLi ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus AttentionsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680672(11041-11050)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680672
Thakur NGouripeddi PLi B(2024)Graph(Graph): A Nested Graph-Based Framework for Early Accident Anticipation2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00736(7518-7526)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00736
Wang TChen KChen GLi BLi ZLiu ZJiang C(2024)GSC: A Graph and Spatio-Temporal Continuity Based Framework for Accident AnticipationIEEE Transactions on Intelligent Vehicles10.1109/TIV.2023.32571699:1(2249-2261)Online publication date: Jan-2024
https://doi.org/10.1109/TIV.2023.3257169
Show More Cited By

Index Terms

Predicting Traffic Accidents with Event Recorder Data
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Survival analysis

Recommendations

The impact of traffic accidents on traffic capacity in weaving area of highway under the intelligent connected vehicle environment: The impact of traffic accidents on traffic capacity in weaving area of highway under the intelligent connected vehicle environment
Abstract
Connected automated vehicles (CAV) will become an important part of future traffic flows. In the highway weaving area, the road conditions are complex and the possibility of traffic accidents is relatively high. In this paper, a cellular automaton ...
Characteristics of Urban Road Non-recurrent Traffic Congestion Based on Floating Car Data
EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

Non-recurrent traffic congestion caused by traffic accidents is one of the most important factors that affect the operational efficiency of urban road networks. In this paper, the floating car data (FCD) and the traffic accident data in Wuhan were ...
Analysis of factors influencing severity of truck turning traffic accidents based on ordered Logit model
CAIBDA '24: Proceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms

To comprehensively analyze the factors influencing the severity of truck turning traffic accidents, we selected data from 283 such accidents in Tianjin from 2010 to 2013. The severity of accidents was taken as the dependent variable, while 13 potential ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

PredictGIS'19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility

November 2019

81 pages

ISBN:9781450369640

DOI:10.1145/3356995

Editors:
Satish V. Ukkusuri
Purdue University, IN, USA
,
Kaoru Sezaki
University of Tokyo, Japan
,
Takahiro Yabe
Purdue University, IN, USA
,
Kota Tsubouchi
Yahoo Japan Corporation, Tokyo, Japan

Copyright © 2019 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

SIGSPATIAL '19

Sponsor:

SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5, 2019

IL, Chicago, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
831
Total Downloads

Downloads (Last 12 months)173
Downloads (Last 6 weeks)26

Reflects downloads up to 13 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Liao HSun HShen HWang CTian CTam KLi LXu CLi ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus AttentionsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680672(11041-11050)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680672
Thakur NGouripeddi PLi B(2024)Graph(Graph): A Nested Graph-Based Framework for Early Accident Anticipation2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00736(7518-7526)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00736
Wang TChen KChen GLi BLi ZLiu ZJiang C(2024)GSC: A Graph and Spatio-Temporal Continuity Based Framework for Accident AnticipationIEEE Transactions on Intelligent Vehicles10.1109/TIV.2023.32571699:1(2249-2261)Online publication date: Jan-2024
https://doi.org/10.1109/TIV.2023.3257169
Liao HLi YLi ZBian ZLee JCui ZZhang GXu C(2024)Real-time accident anticipation for autonomous driving through monocular depth-enhanced 3D modelingAccident Analysis & Prevention10.1016/j.aap.2024.107760207(107760)Online publication date: Nov-2024
https://doi.org/10.1016/j.aap.2024.107760
Bin Rozaimi IZaman FAbdullah SAbidin HMazalan L(2023)Driver Drowsiness Detection Using Deep Learning Models Based On Different Camera Positions2023 11th International Conference on Information and Communication Technology (ICoICT)10.1109/ICoICT58202.2023.10262759(611-616)Online publication date: 23-Aug-2023
https://doi.org/10.1109/ICoICT58202.2023.10262759
Yi CHuang TYe HZhan D(2023)Improved Dynamic Spatial-Temporal Attention Network for Early Anticipation of Traffic Accidents2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)10.1109/ICMEW59549.2023.00020(81-86)Online publication date: Jul-2023
https://doi.org/10.1109/ICMEW59549.2023.00020
Mahmood FJeong DRyu J(2023)A New Approach to Traffic Accident Anticipation With Geometric Features for Better GeneralizabilityIEEE Access10.1109/ACCESS.2023.325999211(29263-29274)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3259992
Liu WZhang TLu YChen JWei L(2023)THAT-Net: Two-layer Hidden State Aggregation based Two-Stream Network for Traffic Accident PredictionInformation Sciences10.1016/j.ins.2023.03.075Online publication date: Mar-2023
https://doi.org/10.1016/j.ins.2023.03.075
Peralta AZatarain-Aceves HCaro K(2023)Technological Solutions for Collecting, Analyzing, and Visualizing Traffic Accidents: A Mapping ReviewProceedings of Eighth International Congress on Information and Communication Technology10.1007/978-981-99-3043-2_54(669-679)Online publication date: 1-Sep-2023
https://doi.org/10.1007/978-981-99-3043-2_54
Nidamanuri JNibhanupudi CAssfalg RVenkataraman H(2022)A Progressive Review: Emerging Technologies for ADAS Driven SolutionsIEEE Transactions on Intelligent Vehicles10.1109/TIV.2021.31228987:2(326-341)Online publication date: Jun-2022
https://doi.org/10.1109/TIV.2021.3122898
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten