电动滑板车骑行中酒精影响检测及虚警控制
In-Ride Alcohol-Impairment Detection in E-Scooterists with False-Alarm Control
- Voi Technology AB（沃伊科技公司）
- KTH Royal Institute of Technology（瑞典皇家理工学院）
- Chalmers University of Technology（查尔姆斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出一种基于车载传感器的电动滑板车骑手酒精影响检测器，其虚警率有可证明边界，实验显示该检测器能有效识别受酒精影响的骑行，且嵌入式实现满足实时要求，为相关干预措施奠定基础。
中文摘要 AI 辅助
共享电动滑板车服务已成为广泛采用的城市交通方式。尽管大多数用户骑行时行为负责，但酒精中毒是导致严重碰撞的因素之一。然而，现有对策仅限于单点反应测试和夜间禁令（完全暂停服务）。本文提出一种新方法，利用车载传感器在行程进行中评估骑手，一旦积累足够的影响证据就发出警报。具体而言，我们引入一种基于惯性和油门测量的检测器，其虚警率具有可证明的边界。对来自141次骑行的传感器数据进行实验，其中25名参与者分别在清醒状态和两个目标血液酒精浓度水平下骑行，结果证实该边界成立，而基线模型和消融实验要么超出该边界，要么丧失检测性能，部分情况下还会延迟警报。在0.023的边界下，该检测器识别出91%的较高浓度骑行和50%的较低浓度骑行，中位检测时间分别为25秒和27秒。我们进一步表明，嵌入式实现满足实时要求，使缓解措施可在车载执行，无需将数据传出车辆。总体而言，本研究为尽快干预受影响骑手奠定基础，使清醒用户无需承担骑行前测试的负担或夜间服务暂停的影响，同时让运营商可根据用户体验权衡虚警预算。
英文摘要
Shared e-scooter services have become a widely adopted urban transport mode. While most users ride responsibly, alcohol intoxication stands out among the factors contributing to severe crashes. Nonetheless, countermeasures remain limited to single-point reaction tests and night bans that suspend the service altogether. This paper proposes a new approach in which onboard sensors evaluate the rider as the trip unfolds, raising an alarm as soon as enough evidence of impairment has accumulated. Specifically, we introduce a detector that operates on inertial and throttle measurements, with a provable bound on the rate of false alarms. Experiments on sensor data from 141 rides, in which 25 participants rode while sober and at two target blood alcohol concentration levels, confirm that the bound holds, whereas baselines and ablations either exceed it or lose detection performance, and in some cases delay the alarm. At a bound of 0.023, the detector identifies 91% of the rides performed at the higher concentration and 50% of those at the lower one, with median detection times of 25 and 27 seconds, respectively. We further show that an embedded implementation meets the real-time requirement, making mitigation actions feasible onboard, without requiring data to leave the vehicle. Overall, this work lays the ground for interventions that reach impaired riders as soon as possible, sparing the sober ones the burden of a pre-ride test or the suspension of the service at night, while letting operators budget false alarms against user experience.
