Safety and Comfort Enhancement with Radar for a Bicycle Assistance System

Hagelen, Manfred; Jetten, Rainer; Kassner, J.; Kulke, R.

doi:10.23919/irs.2019.8768109

Cited by 6 publications

(2 citation statements)

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“…A miniaturized system [5] detects and tracks motorized vehicles passed by a bicycle rider while maintaining minimum power consumption. The authors of [6] propose the development of a 24 GHz FMCW front-end radar for Pedal Electric Cycles (Pedelec) configured with several antennas to identify possible threats based on a range-velocity analysis and multi-target object tracker with a Kalman Filter.…”

Section: Introductionmentioning

confidence: 99%

Advancing Cycling Safety: On-Bike Alert System Utilizing Multi-Layer Radar Point Cloud Clustering for Coarse Object Classification

Omri,

Benothman,

Sayahi

et al. 2024

Sensors

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Cyclists are considered to be vulnerable road users (VRUs) and need protection from potential collisions with cars and other vehicles induced by unsafe driving, dangerous road conditions, or weak cycling infrastructure. Integrating mmWave radars into cycling safety measures presents an efficient solution to this problem given their compact size, low power consumption, and low cost compared to other sensors. This paper introduces an mmWave radar-based bike safety system designed to offer real-time alerts to cyclists. The system consists of a low-power radar sensor affixed to the bicycle, connected to a micro-controller, and delivering a preliminary classification of detected obstacles. An efficient two-level clustering based on the accumulation of radar point clouds from multiple frames with a temporal projection from previous frames into the current frame is proposed. The clustering is followed by a coarse classification algorithm in which we use relevant features extracted from the resulting clusters. An annotated RadBike dataset composed of radar point cloud data synchronized with RGB camera images is developed to evaluate our system. The two-level clustering outperforms the DBSCAN algorithm, achieving a v-measure score of 0.91, compared to 0.88 with classical DBSCAN. Different classifiers, including decision trees, random forests, support vector machines (SVMs), and AdaBoost, have been assessed, with an overall accuracy of 87% for the three main object classes: four-wheeled, two-wheeled, and others. The system has the ability to improve rider safety on the road and substantially reduce the frequency of incidents involving cyclists.

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Section: Introductionmentioning

confidence: 99%

Advancing Cycling Safety: On-Bike Alert System Utilizing Multi-Layer Radar Point Cloud Clustering for Coarse Object Classification

Omri,

Benothman,

Sayahi

et al. 2024

Sensors

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show abstract

“…Safe riding support for senior cyclists had been investigated with radar technology [3]. With the increase of bicycle-to-pedestrian accidents, 24 GHz-band frequency modulated continuous wave multi-input multi-output (FMCW-MIMO) radar has been tested to prevent forward collisions of the bicycle and support visibility [4,5], where objects (e.g., road obstacles, building, and cars) were detected in large space, not in relatively narrow bicycle road. On the other, millimeter-wave (mm) radar has recently been put to practical use as a support for the safe driving of automobiles which has the advantage of being compact, inexpensive to design, and higher distance resolution compared with those features of the 24 GHz-band.…”

Section: Introductionmentioning

confidence: 99%

An experimental study of obstacles and pedestrian classification by relative velocity distribution on urban-narrow bicycle-pedestrian road with 77 GHz FMCW radar

et al. 2021

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In recent years, the number of people using the bicycle as a means of transportation has increased due to heightened health awareness and environmental protection and COVID-19 pandemic, instead of public such as train and bus. Further, bicycle-related traffic accidents have rapidly increased due to the rapid growth of the food-delivery business with bicycle use, and thus countermeasures are urgently needed. In this paper, we propose a method for a bicycle to detect pedestrian separating from road obstacles such as guard poles and guardrails installed in an urban-narrow bicycle-pedestrian road with the use of frequency modulated continuous wave multi-input multi-output (FMCW-MIMO) radar. In the proposed method, from the distribution of the relative velocity of the detected objects, the objects gathering around the mode of the relative-velocity distribution are judged as the road obstacles, and the objects far from the mode are judged as pedestrian. In an experiment, a bicycle equipped with 77 GHzband FMCW-MIMO radar, ran at a speed of 2 m/s and a pedestrian walked ahead at a speed of 1 m/s in a narrow bicycle-pedestrian road (school zone) in central Tokyo. Experimental result shows that the pedestrian was detected with about 1m/s-higher relative velocity than around 1.5 m/s of mode relative velocity, where road obstacles group distributed in the range of ± 0.5 m/s.

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How do cyclists experience a context-aware prototype warning system? Assessing perceived safety, perception and riding behaviour changes through a field study

Kapousizis,

Jutte,

Ulak

et al. 2025

Journal of Cycling and Micromobility Research

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Safety and Comfort Enhancement with Radar for a Bicycle Assistance System

Cited by 6 publications

References 0 publications

Advancing Cycling Safety: On-Bike Alert System Utilizing Multi-Layer Radar Point Cloud Clustering for Coarse Object Classification

Advancing Cycling Safety: On-Bike Alert System Utilizing Multi-Layer Radar Point Cloud Clustering for Coarse Object Classification

An experimental study of obstacles and pedestrian classification by relative velocity distribution on urban-narrow bicycle-pedestrian road with 77 GHz FMCW radar

How do cyclists experience a context-aware prototype warning system? Assessing perceived safety, perception and riding behaviour changes through a field study

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