More Web Proxy on the site http://driver.im/

Article

Bayesian networks and traffic accident reconstruction

Authors:

Jianping PeiAuthors Info & Claims

ICAIL '03: Proceedings of the 9th international conference on Artificial intelligence and law

Pages 171 - 176

https://doi.org/10.1145/1047788.1047829

Published: 24 June 2003 Publication History

Abstract

The attempt to draw rational conclusions about a road accident can be viewed as a problem in uncertain reasoning about a particular event, to which developments in the modeling of uncertain reasoning for artificial intelligence can be applied. Physical principles can be used to develop a structural model for the accident, and this model can then be combined with an expert assessment of prior uncertainty concerning the model's variables. Posterior probabilities, given evidence collected at the accident scene, can then be computed using Bayes theorem. Truth conditions for counterfactual claims about the accident can then be defined using a "possible worlds" semantics, and used to rigorously implement a "but for" test of whether or not a speed limit violation could be considered a cause of the accident.

References

[1]

Baker, J. Causes and contributing factors in traffic accidents. In Traffic Accident Reconstruction. (L. Fricke ed.) Northwestern University Traffic Institute, 1990.

[2]

Baker, J., and Fricke, L. Process of traffic accident reconstruction. In Traffic Accident Reconstruction. (L. Fricke ed.) Northwestern University Traffic Institute, 1990.

[3]

Balke, A. and Pearl, J. Probabilistic evaluation of counterfactual queries. Proceedings of 12th National Conference on Artificial Intelligence, AAAI Press, 1994, 230--237.

Digital Library

[4]

Davis, G. Using Bayesian networks to identify the causal effect of speeding in individual vehicle/pedestrian collisions. Proceedings of 17th Conference on Uncertainty in Artificial Intelligence. (J. Breese and D. Koller, eds.) Morgan Kaufmann 2001, 105--111.

Digital Library

[5]

Davis, G., Sanderson, K., and Davuluri, S. Development and Testing of a Vehicle/Pedestrian Collision Model for Neighborhood Traffic Control. Report 2002--03, Minnesota Dept. of Transportation, 2002.

[6]

Eubanks, J. and Hill, P. Pedestrian Accident Reconstruction and Litigation, Lawyers and Judges Publishing Co. 1998.

[7]

Evett, I. Expert evidence and forensic misconceptions of the nature of exact science. Science and Justice, 36, 1996, 118--122.

[8]

Fambro, D., Koppa, R., Picha, D., and Fitzpatrick, K. Driver perception-brake response in stopping sight distance situations. Proceedings of 78th Annual Meeting of Transportation Research Board, paper 981410, 1998.

[9]

Fricke, L. Traffic Accident Reconstruction. Northwestern University Traffic Institute, 1990.

[10]

Garrott, W., and Guenther, D. Determination of precrash parameters from skidmark analysis. Transportation Research Record, 893, 1982, 38--46.

[11]

Greatrix, G. AI lecture notes 2: Analysis of a simple accident. Accident Investigation Lecture Notes, scratchy.spods.co.uk/~greatrix/AINotes.html, December 27, 2002.

[12]

Halpern, J. An analysis of first-order logics of probability. Artificial Intelligence, 46, 1990, 311--350.

Digital Library

[13]

Hoogstrate, A. and Spek, A. Monte Carlo simulation and inference in accident reconstruction. Presented at 5th International Conference on Forensic Statistics, Venice, 2002.

[14]

Jensen, F. An Introduction to Bayesian Networks, Springer, 1996.

Digital Library

[15]

Lindley, D. Subjective probability, decision analysis and their legal consequences. J. Royal Statistical Society part A, 154, 1991, 83--92.

[16]

Neptune, J., Flynn, J., Chavex, P., and Underwood, H. Speed from skids: a modern approach. Accident Reconstruction : Technology and Animation V, SAE Inc, 1995, 189--204.

[17]

Pearl, J. Causality: Models, Reasoning, and Inference, Cambridge University Press, 2000.

Digital Library

[18]

Poole, D. and Raftery, A. Inference for deterministic simulation models: The Bayesian melding approach. J. American Statistical Association, 95, 2000, 1244--1255.

[19]

Raftery, A., Givens, G., and Zeh, J. Inference from a deterministic population dynamics model for Bowhead whales. J. American Statistical Association, 90, 1995, 402--416

[20]

Reed, W., and Keskin, A. Vehicular deceleration and its relationship to friction. SAE Technical Paper 890736, SAE Inc. 1989.

[21]

Rudram, D., and Lambourn, R. The scientific investigation of road accidents. Journal of Occupational Accidents, 3, 1981, 177--185.

[22]

Spiegelhalter, D., Thomas, A., and Best, N. WinBUGS Version 1.3 User Manual, MRC Biostatistics Unit, Oxford University, 2000.

[23]

Stewart-Morris, M. Real time, accurate recall, and other myths. Forensic Accident Investigation: Motor Vehicles, (T. Bohan and A. Damask, eds.) Michie-Butterworth, 1997, 413--438.

[24]

Taroni, F., Aitken, C., and Garbolino, P. DeFinetti's subjectivism, the assessment of probabilities and the evaluation of evidence: a commentary for forensic scientists. Science and Justice, 41, 2001, 145--150.

[25]

Traffic Institute, Accident Investigation Manual, Northwestern University, 1946.

[26]

Wolpert, R. Comment on Inference from a deterministic population dynamics model for Bowhead whales. J. American Statistical Association, 90, 1995, 426--427.

[27]

Wood, D., and O'Riordain, S. Monte Carlo simulation methods applied to accident reconstruction and avoidance analysis. Accident Reconstruction: Technology and Animation IV, SAE, Inc, 1994, 129--136.

Cited By

Vreeswijk G(2018)Review of "Bayesian artificial intelligence", by Kevin B. Korb, Ann E. Nicholson, Chapman & Hall/CRC, 2003Artificial Intelligence and Law10.1023/B:ARTI.0000045970.25670.2511:4(289-298)Online publication date: 25-Dec-2018
https://dl.acm.org/doi/10.1023/B%3AARTI.0000045970.25670.25
Jin JDeng Y(2017)A comparative study on traffic violation level prediction using different models2017 4th International Conference on Transportation Information and Safety (ICTIS)10.1109/ICTIS.2017.8047913(1134-1139)Online publication date: Aug-2017
https://doi.org/10.1109/ICTIS.2017.8047913
Zhu ZChen XXiong CZhang L(2017)A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choiceTransportation10.1007/s11116-017-9770-645:5(1499-1522)Online publication date: 18-Mar-2017
https://doi.org/10.1007/s11116-017-9770-6
Show More Cited By

Recommendations

Vision-based highway traffic accident detection
AIIPCC '19: Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing

The highway traffic scene is relatively simple, the traffic flow is small but the speed of traffic is fast. The traffic accidents on highways are sudden and harmful. Based on highway traffic monitoring video, this paper uses machine learning to ...
A Study on Protecting of the Far Side Occupants Based on Road Traffic Accident Reconstruction
ICDMA '12: Proceedings of the 2012 Third International Conference on Digital Manufacturing & Automation

A study on injuries of far side passenger in vehicle side impact was carried out by using a real road accident. Firstly, the trace of the accident car was reproduced using PC-Crash code. The impact velocity of the passenger car calculated from PC-Crash ...
Geometry features measurement of traffic accident for reconstruction based on close-range photogrammetry

This paper studies the feasibility of investigating a traffic accident and offering initial data for traffic accident reconstruction (TAR) using a photogrammetric technique. Compared with the conventional roller tape applied by the traffic police of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ICAIL '03: Proceedings of the 9th international conference on Artificial intelligence and law

June 2003

304 pages

ISBN:1581137478

DOI:10.1145/1047788

Conference Chair:
John Zeleznikow
The Edinburgh Law School, University of Edinburgh, Scotland, UK
,
Program Chair:
Giovanni Sartor
CIRSFID, University of Bologna, Italy

Copyright © 2003 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence
West Group, Thomson Legal & Regulatory
The International Association for Artificial Intelligence and Law
The Joseph Bell Centre for Forensic Statistics and Legal Reasoning
The University of Edinburgh School of Law

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 June 2003

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Acceptance Rates

Overall Acceptance Rate 69 of 169 submissions, 41%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
627
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Vreeswijk G(2018)Review of "Bayesian artificial intelligence", by Kevin B. Korb, Ann E. Nicholson, Chapman & Hall/CRC, 2003Artificial Intelligence and Law10.1023/B:ARTI.0000045970.25670.2511:4(289-298)Online publication date: 25-Dec-2018
https://dl.acm.org/doi/10.1023/B%3AARTI.0000045970.25670.25
Jin JDeng Y(2017)A comparative study on traffic violation level prediction using different models2017 4th International Conference on Transportation Information and Safety (ICTIS)10.1109/ICTIS.2017.8047913(1134-1139)Online publication date: Aug-2017
https://doi.org/10.1109/ICTIS.2017.8047913
Zhu ZChen XXiong CZhang L(2017)A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choiceTransportation10.1007/s11116-017-9770-645:5(1499-1522)Online publication date: 18-Mar-2017
https://doi.org/10.1007/s11116-017-9770-6
Karimnezhad AMoradi F(2016)Road accident data analysis using Bayesian networksTransportation Letters10.1080/19427867.2015.11319609:1(12-19)Online publication date: 10-Feb-2016
https://doi.org/10.1080/19427867.2015.1131960
Nieves JSantos IPenya YRojas SSalazar MBringas P(2009)Mechanical properties prediction in high-precision foundry production2009 7th IEEE International Conference on Industrial Informatics10.1109/INDIN.2009.5195774(31-36)Online publication date: Jun-2009
https://doi.org/10.1109/INDIN.2009.5195774
Penya YBringas PZabala A(2008)Advanced fault prediction in high-precision foundry production2008 6th IEEE International Conference on Industrial Informatics10.1109/INDIN.2008.4618372(1672-1677)Online publication date: Jul-2008
https://doi.org/10.1109/INDIN.2008.4618372
Penva YBringas P(2008)Integrating network misuse and anomaly prevention2008 6th IEEE International Conference on Industrial Informatics10.1109/INDIN.2008.4618168(586-591)Online publication date: Jul-2008
https://doi.org/10.1109/INDIN.2008.4618168
Muecke NStranieri A(2007)An argument structure abstraction for bayesian belief networksProceedings of the fourth Asia-Pacific conference on Comceptual modelling - Volume 6710.5555/1274453.1274461(35-40)Online publication date: 30-Jan-2007
https://dl.acm.org/doi/10.5555/1274453.1274461

View Options

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents