都市空間センシングとモビリティ解析

E w -601

Micro-Mobility Safety［ Advanced Cyclist-Assistance Schemes ］
Zengyi Han, Liqiang Xu, Xuefu Dong, Zhen Zhu, Yuuki Nishiyama and Kaoru Sezaki

東京大学 生産技術研究所 瀬崎·西山研究室
Cycling must be safe, and perceived as such, if micro-mobility trips by all populations are to increase, and the benefits in traffic
decongestion and carbon emission cut are to be realized. WHO estimates that 40,000 cyclists died in road accident in 2016. Thereis an urgent need to address this problem by low-cost and robust safeguard systems.

Riding Direction
1s0s

Head Dir.
2s

3s

Visual Search Mon.
IMU
on Helmet

Accel.+Gyro.
Head Move.

Predict

Distracted Behavior Detection

I2023 IEEE
HeadSense WoWMoM

IMU
on BikeHEADSENSE —————————
We present HeadSense, a helmet-based
system that leverages the inertial motion
unit (IMU) to recognize 4 distracted
behaviors: checking handlebar-mounted
devices, using smartphones, attracting to
the roadside element, and abreast riding.
The feasibility lies as followed:• The head dynamic is a strong indicatorof the rider‘s eyesight / attention. And thehelmet, as an essential bicycle
accessory, follows the same movement
pattern as rider's head.
• the rider’s head shows unique motion
patterns
during
distractedridingbehaviors. These patterns consist of aseries of abnormal head movements.

HeadMonIEEE PerCom’23
IEEE ICDCS’24!

Accel.+Gyro.+Mag

Bike Move.

Maneuver Prediction

Real-time Single-handed Distracted Behavior Detection
DoubleCheck IEEE SMC’22
DOUBLECHECK ———————HEADMON ——————————
Detection of ongoing maneuver may beless effective in accident prevention. In
this work, we take a step further toexplore the feasibility of using riders’ head
dynamics to predict their riding
maneuvers with two key observations:• Rider needs to observe the traffic
situation in advance based on their riding
maneuver intentions.
• For different maneuver intentions, therider's head dynamics (such as turning
left and right) are also different.

DoubleCheck is a method that utilizes ahandlebar-mounted smartphone to detect
single-handed cycling and followed
distracting secondary tasks. The work was
established on the premise that singlehanded cycling undermines the stability ofhandlebar during cycling. Preliminary datashows that For both acceleration andangular speed:• The signal has denser power over thefrequency band during single-handedcycling.
• The signals contain periodic components
Accordingly, we adopt Autoregressive
Model, featuring robust performance inextracting features of periodic time-series
signal.

• Autoregressive Model

Evaluation results show HeadSense cansegment visual search into episodes with anaccuracy of up to 86.14%. Additionally, fromsequences of episodes, it can effectively
detect distracted riding behaviors at anaverage precision of 85.04%.

We constructed an Attention-based
network to solve the prediction problem.
The precision of riding maneuver
prediction is at least 0.80 under 4seconds time gap. We also finds that theaccuracy would be improved with longer
detection window size.
The results indicate a novel start on
improving micro-mobility safety.

Experiment with 22 participants onasphalt and pavement demonstrated that
DoubleCheck achieves an F1-score of0.96 for hand detection and 0.69 for
distraction recognition