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.

全球而言，摩托车吸引了广泛及多样的用户。然而，由于摩托车事故严重伤亡率远远超过乘用车事故，人们致力于提高被动安全系统。该论文通过模拟事故和驾驶操作，收集数据并训练机器学习分类模型，研究了机器学习算法在可靠检测即将发生的碰撞方面的适用性，并通过多种代表性和应用导向的标准评估和比较它们的性能。