Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles.

Ying Zhao, Haijun Li, Yan Huang, Junyu Hang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

The escalating burden of cyclist road injuries among reproductive-aged women in china: a longitudinal and gender-specific analysis of the global burden of disease study 1990-2021.BMC public health · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ying ZhaoSchool of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China.

Haijun LiSchool of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China.

Yan HuangSchool of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China.

Junyu HangMOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.

Funding

Key Research and Development Project of Gansu Provincial Science and Technology Department 22YF7GA142Systematic Major Project of China National Railway Group P2021S012

6 · The paper itself

Abstract

The rapid growth in the number of electric bicycles (e-bicycles) has greatly improved daily commuting for residents, but it has also increased traffic collisions involving e-bicycles. This study aims to develop an autonomous emergency braking (AEB) system for e-bicycles to reduce rear-end collisions. A framework for the AEB system composed of the risk recognition function and collision avoidance function was designed, and an e-bicycle following model was established. Then, numerical simulations were conducted in multiple scenarios to evaluate the effectiveness of the AEB system under different riding conditions. The results showed that the probability and severity of rear-end collisions involving e-bicycles significantly decreased with the application of the AEB system, and the number of rear-end collisions resulted in a 68.0% reduction. To more effectively prevent rear-end collisions, a low control delay (delay time) and suitable risk judgment criteria (TTC threshold) for the AEB system were required. The study findings suggested that when a delay time was less than or equal to 0.1 s and the TTC threshold was set at 2 s, rear-end collisions could be more effectively prevented while minimizing false alarms in the AEB system. Additionally, as the deceleration rate increased from 1.5 m/s

Indexed as

autonomous emergency braking systeme-bicycle following modelelectric bicyclerear-end collisionsafety surrogate measure

Identifiers

PMID38202997

DOI10.3390/s24010137

PMCPMC10781380

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.