# On using Machine Learning Algorithms for Motorcycle Collision Detection

arXiv: https://arxiv.org/abs/2403.09491 · HTML: https://mlsanity.com/paper/2403.09491/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 37 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Built on by (most cited first, 4 of 4)

- [Towards Trustworthy Report Generation: A Deep Research Agent with Progressive Confidence Estimation and Calibration](https://mlsanity.com/paper/2604.05952/) (2026)
- [MSTN: A Lightweight and Fast Model for General TimeSeries Analysis](https://mlsanity.com/paper/2511.20577/) (2025)
- [Predicting Time Pressure of Powered Two-Wheeler Riders for Proactive Safety Interventions](https://mlsanity.com/paper/2601.03173/) (2026)
- [MotoSafety: Edge-AI with Learned Temporal Importance for Two-Wheeler Collision Risk Assessment Under Time Pressure](https://mlsanity.com/paper/2608.17823/) (2026)

## Abstract (arXiv)

Globally, motorcycles attract vast and varied users. However, since the rate of severe injury and fatality in motorcycle accidents far exceeds passenger car accidents, efforts have been directed toward increasing passive safety systems. Impact simulations show that the risk of severe injury or death in the event of a motorcycle-to-car impact can be greatly reduced if the motorcycle is equipped with passive safety measures such as airbags and seat belts. For the passive safety systems to be activated, a collision must be detected within milliseconds for a wide variety of impact configurations, but under no circumstances may it be falsely triggered. For the challenge of reliably detecting impending collisions, this paper presents an investigation towards the applicability of machine learning algorithms. First, a series of simulations of accidents and driving operation is introduced to collect data to train machine learning classification models. Their performance is henceforth assessed and compared via multiple representative and application-oriented criteria.