Abstract
The majority of the prevailing collision detection and avoidance systems render evasive maneuvers for averting front and rear vehicle collisions. This paper introduces a mid vehicle collision detection and avoidance system with a constraint-free condition that produces mid vehicle maneuvers, particularly when jammed between the front and rear vehicles. Accordingly, three novel path estimation models based on crisp, fuzzy and fuzzy regression logic are blended to formulate a mid (host) vehicle collision detection and avoidance system. At the onset, the crisp model fits an offset-based curvilinear path for the mid vehicle to avoid edge collisions. The realized crisp model is later fuzzified to address more antecedents and accordingly deliver consequents for enhancing path estimation. Finally, the fuzzified model is regressed to obtain a good fitness for real-road conditions. The fused fuzzy regression renders an approximate version of the actual road strategy to obtain collision-free trajectories. This concept is later intrinsically extended to parallel parking in reverse direction. Simulation studies using coefficient of determination \((R^{2} )\) and mean square error on the field, real (next generation simulation-NGSIM) dataset reveals the goodness and the path closeness of the proposed system rendered by the novelly blended models tuned at each observation using the optimum h-uncertain factor. Also, relative mean square error analysis with state-of-art CDAS reveals the superiority of MCDAS, thereby making it amicable for real-road scenarios.
Similar content being viewed by others
References
National Transportation Safety Board (2015) The use of forward collision avoidance systems to prevent and mitigate rear-end crashes. Special investigation report. NTSB/SIR-15-01. Washington, DC
Kim J-H, Kum D-S (2015) Threat prediction algorithm based on local path candidates and surrounding vehicle trajectory predictions for automated driving vehicles. In: 2015 IEEE intelligent vehicles symposium (IV). IEEE, pp 1220–1225
Zhang S, Simkani M, Zadeh MH (2011) Automatic vehicle parallel parking design using fifth degree polynomial path planning. In: 2011 IEEE vehicular technology conference (VTC Fall). IEEE, pp 1–4
Hoffmann GM, Tomlin CJ, Montemerlo M, Thrun S (2007) Autonomous automobile trajectory tracking for off-road driving: controller design, experimental validation and racing. In: 2007 American control conference. IEEE, pp 2296–2301
Kim J, Jo K, Lim W et al (2015) Curvilinear-coordinate-based object and situation assessment for highly automated vehicles. IEEE Trans Intell Transp Syst 16:1559–1575. https://doi.org/10.1109/TITS.2014.2369737
Parate SM, V Seshy Babu, Swarup S (2014) Night time rear end collision avoidance system using SMPTE-C standard and VWVF. In: 2014 IEEE international conference on vehicular electronics and safety. IEEE, pp 17–21
Rezaei M, Terauchi M, Klette R (2015) Robust vehicle detection and distance estimation under challenging lighting conditions. IEEE Trans Intell Transp Syst 16:2723–2743. https://doi.org/10.1109/TITS.2015.2421482
Chen S, Sun J (2013) Dynamic speed guidance for active highway signal coordination: roadside against in-car strategies. IET Intell Transp Syst. https://doi.org/10.1049/iet-its.2012.0084
Chu K, Lee M, Sunwoo M (2012) Local path planning for off-road autonomous driving with avoidance of static obstacles. IEEE Trans Intell Transp Syst 13:1599–1616. https://doi.org/10.1109/TITS.2012.2198214
Anderson SJ, Karumanchi SB, Iagnemma K (2012) Constraint-based planning and control for safe, semi-autonomous operation of vehicles. In: 2012 IEEE intelligent vehicles symposium. IEEE, pp 383–388
Weiskircher T, Ayalew B (2015) Frameworks for interfacing trajectory tracking with predictive trajectory guidance for autonomous road vehicles. In: 2015 American control conference (ACC). IEEE, pp 477–482
Kim T, Jeong H-Y (2014) A novel algorithm for crash detection under general road scenes using crash probabilities and an interactive multiple model particle filter. IEEE Trans Intell Transp Syst 15:2480–2490. https://doi.org/10.1109/TITS.2014.2320447
Milanés V, Pérez J, Godoy J, Onieva E (2012) A fuzzy aid rear-end collision warning/avoidance system. Expert Syst Appl 39:9097–9107. https://doi.org/10.1016/J.ESWA.2012.02.054
Wang X, Fu M, Ma H, Yang Y (2015) Lateral control of autonomous vehicles based on fuzzy logic. Control Eng Pract 34:1–17. https://doi.org/10.1016/J.CONENGPRAC.2014.09.015
Hojati M, Bector CR, Smimou K (2005) A simple method for computation of fuzzy linear regression. Eur J Oper Res 166:172–184. https://doi.org/10.1016/J.EJOR.2004.01.039
Abdullah L, Zamri N (2012) Road traffic accidents models using threshold levels of fuzzy linear regression. In: 2012 International conference on statistics in science, business and engineering (ICSSBE). IEEE, pp 1–5
Chan KY, Engelke U (2017) Varying spread fuzzy regression for affective quality estimation. IEEE Trans Fuzzy Syst 25:594–613. https://doi.org/10.1109/TFUZZ.2016.2566812
Shakouri H, Nadimi GR, Ghaderi F (2007) Fuzzy linear regression models with absolute errors and optimum uncertainty. In: 2007 IEEE international conference on industrial engineering and engineering management. IEEE, pp 917–921
Chen F, Chen Y, Zhou J, Liu Y (2016) Optimizing h value for fuzzy linear regression with asymmetric triangular fuzzy coefficients. Eng Appl Artif Intell 47:16–24. https://doi.org/10.1016/J.ENGAPPAI.2015.02.011
Zeng W, Feng Q, Li J (2017) Fuzzy least absolute linear regression. Appl Soft Comput 52:1009–1019. https://doi.org/10.1016/J.ASOC.2016.09.029
Li J, Zeng W, Xie J, Yin Q (2016) A new fuzzy regression model based on least absolute deviation. Eng Appl Artif Intell 52:54–64. https://doi.org/10.1016/J.ENGAPPAI.2016.02.009
Chen L-H, Hsueh C-C (2007) A mathematical programming method for formulating a fuzzy regression model based on distance criterion. IEEE Trans Syst Man Cybern Part B 37:705–712. https://doi.org/10.1109/TSMCB.2006.889609
Chang S-T, Lu K-P, Yang M-S (2015) Fuzzy change-point algorithms for regression models. IEEE Trans Fuzzy Syst 23:2343–2357. https://doi.org/10.1109/TFUZZ.2015.2421072
Tsaur R-C, Wang H-F (1999) Fuzzy goal programming for solving fuzzy regression equation. In: FUZZ-IEEE’99. 1999 IEEE international fuzzy systems. conference proceedings (Cat. No.99CH36315), vol 1. IEEE, pp 11–15
Tau Lee H, Hua Chen S (2001) Fuzzy regression model with fuzzy input and output data for manpower forecasting. Fuzzy Sets Syst 119:205–213. https://doi.org/10.1016/S0165-0114(98)00382-0
Liu Y, Chen Y, Zhou J, Zhong S (2015) Fuzzy linear regression models for QFD using optimized h values. Eng Appl Artif Intell 39:45–54. https://doi.org/10.1016/J.ENGAPPAI.2014.11.007
Stanislas L, Peynot T (2015) Characterisation of the Delphi electronically scanning radar for robotics applications. In: Australasian conference on robotics and automation, 2–4 December 2015. Canberra, A.C.T
Klotz M (2002) An automotive short range high resolution pulse radar network. Shaker Verlag GmbH, Germany
Driggs-Campbell K, Govindarajan V, Bajcsy R (2017) Integrating intuitive driver models in autonomous planning for interactive maneuvers. IEEE Trans Intell Transp Syst 18:3461–3472. https://doi.org/10.1109/TITS.2017.2715836
Acknowledegments
The authors of this manuscript are grateful to Shevade R.D, Manager, Powertrain Diesel, Delphi Automotive Systems for providing field data for performance validation and valuable suggestions in devising the intended model.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Prabhakaran, N., Sudhakar, M.S. Fuzzy curvilinear path optimization using fuzzy regression analysis for mid vehicle collision detection and avoidance system analyzed on NGSIM I-80 dataset (real-road scenarios). Neural Comput & Applic 31, 1405–1423 (2019). https://doi.org/10.1007/s00521-018-3553-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-018-3553-7