Collision warning for bicycles

With the increasing spread of e-bikes, the number of accidents is also rising. An increase of more than 50% compared to last year, to over 2900 accidents, can be observed [BR24]. Many accidents occur due to excessive speed and/or riding into oncoming traffic (oncoming cyclists). A similar development was observed with the introduction of the automobile. Step by step, more and more active safety features (ESP, Emergency Brake Warning, ACC, …) were introduced in addition to passive ones (seat belts, airbags). In the bicycle sector, besides passive safety functions such as safety vests and helmets, active safety functions are not yet widespread.

Overview

In this project, a warning system for cyclists shall be developed. The system shall consist of low-cost components, exterozeptive sensors (e.g. camera, ultrasonic sensors) as well as propriozeptive sensors (e.g. accelerometer, gyroscope), be energy-efficient and compact in design so that it can be mounted unobtrusively on an e-bike.

Project shall be extended to various usecases such as

• Scene interpretation with respect to other cyclists and/or pedestrians
• Classification of unsafe/unstable driving behavior
• Recognition of potential hazards in the environment, e.g. potholes, high curbs,…

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Completed thesis

J. Ruf: Untersuchung einer Distanzmetrik für eine kamerabasierte Fahrrad-Sicherheitsfunktion, July 2021

In this thesis, an efficient object detection algorithm has been implemented to detect cyclist and a criticality metric has been investigated for its real-time capability using a monocular camera running on a Raspberry Pi 3b. Specifically, a machine learning algorithm had to identify relevant features per cyclist and tracked these features over time via the optical flow.

As both, detected cyclist and moving, the consistent identification and tracking of relevant features is a challenge, especially in scenarios with high background noise. As a result, a feature outlier detection is of importance. Various algorithms have been investigated and analyzed for the real-time capability running on a Raspberry Pi 3b.

Based on the scaling of the detected features, a time-to-collision (TTC) can be well estimated. As the results show, the TTC is accurate up to 3 seconds, which is sufficient for the system to warn the cyclist of an upcoming critical situation.

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More information can be found in the EI Portal.