Inevitable Collision States for Motorcycle-to-Car Collision Scenarios

Savino, Giovanni; Giovannini, Federico; Fitzharris, Michael; Pierini, Marco · 2016 · OpenAlex-citations

DOI: 10.1109/tits.2016.2520084

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Summary

This paper addresses the challenge of identifying Inevitable Collision States (ICS) specifically for motorcycles interacting with passenger cars, a gap in existing research that primarily focused on four-wheeled vehicles. The high fatality rate among motorcycle riders and the natural aversion to intrusive safety technologies motivate the development of a "last-resort" triggering criterion for Motorcycle Autonomous Emergency Braking (MAEB). The authors aim to define ICS methods that account for the unique single-track dynamics and maneuverability of motorcycles, ensuring that safety systems activate only when a collision is unavoidable despite extreme avoidance actions. The methodology involves modeling both the host motorcycle and the opponent car as cuboids with mass concentrated at their geometric centers. The authors developed dynamic models for extreme avoidance maneuvers, including pure braking, pure swerving, and combined actions, constrained by physical limits such as road-tire adherence and vehicle geometry. To determine the ICS, the study analyzes a finite set of reference maneuvers within a five-dimensional state space defined by the opponent car’s position, velocity, and heading, and the motorcycle’s velocity. The authors propose two implementation strategies: an online algorithm that computes the ICS region in real-time and an offline look-up table approach that discretizes the state space for faster processing. The look-up table method uses conservative approximations to ensure safety despite discretization errors. The results demonstrate that the proposed ICS identification method accurately captures the characteristics of inevitable collisions, including typical Time to Collision (TTC) values at detection. Analysis of the ICS matrix revealed that TTC is highest when trajectories project toward the center of the opponent car and lowest for sideswipe scenarios. The study validated the method through computer-based simulations using PreScan and Bikesim software, recreating real-world crash scenarios where a car crossed the motorcycle’s path. The simulations confirmed that the theoretical TTC values aligned with simulated data. Furthermore, the authors evaluated an idealized MAEB system triggered by the ICS detection, showing that the system could effectively apply braking when the rider did not intervene and the motorcycle was upright. The significance of this work lies in extending ICS theory to powered two-wheelers, providing a robust foundation for the development of intrusive safety systems like MAEB. By defining precise conditions under which a collision is inevitable, the method supports a last-resort approach that respects rider autonomy while maximizing safety potential. The proposed look-up table and real-time algorithms offer practical pathways for embedding these safety features into motorcycle systems, potentially reducing the high number of severe casualties associated with motorcycle accidents.

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Topics

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• motorcycle crash typology