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)

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.