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This paper addresses the critical problem of slip-induced falls in motorcycles by extending a prior vehicle model to formulate fall suppression as a speed- and surface-condition-dependent optimization problem. A remotely operated motorcycle equipped with a rear-wheel-swing mechanism was used to experimentally validate the approach. The proposed method successfully suppressed falls during challenging slalom and cornering maneuvers on low-friction surfaces.
A novel control strategy effectively prevents motorcycle falls during cornering on slippery surfaces, a long-standing challenge for riders and a major cause of accidents.
Slip-induced falls when riding motorcycles are difficult to avoid even for expert riders and represent a major cause of single-vehicle accidents. Despite their prevalence, no practical solution has yet been proposed to suppress this phenomenon. We previously introduced a motorcycle model applicable to systems with both front- and rear- wheel steering. In this paper, we extend that model and formulate the suppression of slip-induced falls as an optimization problem that distributes assistive control inputs according to vehicle speed and road surface conditions. To evaluate the proposed method, we developed a remotely operated motorcycle and conducted experiments under two scenarios: slalom driving on a low-friction surface at 5.5 m/s, and turning maneuvers on synthetic ice to simulate driving over manholes on rainy days. The results demonstrate that our approach successfully suppresses falls under these challenging conditions.