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SUMMARY:Velosense: Estimating Passing Distance and Curb Distance from Bicy
 cle-Mounted Radar and Camera Data - Michael Schmid
DTSTART;TZID=Europe/Amsterdam:20260918T095000
DTEND;TZID=Europe/Amsterdam:20260918T101500
DTSTAMP:20260918T121422Z
UID:pretalx-crbam-2026-YSHXJB@pretalx.com
DESCRIPTION:Cities and municipalities in Switzerland\, as well as in many 
 European countries\, are developing or improving cycling infrastructure. H
 owever\, much of this infrastructure remains located on roads where cyclis
 ts share space with motorised vehicles. \n\nOne way to assess the effectiv
 eness of infrastructure improvements is by measuring overtaking distance. 
 In this context\, the distance to the curb on the right side of the cyclin
 g infrastructure must also be considered\, as the lateral position of the 
 cyclist—and thus their distance to the curb—influences the overtaking 
 distance on the left. The distance to the curb is also relevant for cyclis
 ts’ perceived sense of safety. \n\nThis project introduces Velosense\, a
  measurement approach designed to support targeted test campaigns using in
 strumented bicycles and to provide direct evidence of how overtaking behav
 iour changes following infrastructure interventions. \n\nVelosense uses a 
 commercially available radar and video device from Garmin. It combines bic
 ycle-mounted radar\, video\, GPS data\, and AI-based image segmentation to
  transform raw ride recordings into structured overtaking events that can 
 be mapped\, validated\, and exported. The key methodological contribution 
 lies in estimating two cyclist-centred spatial metrics that are rarely ava
 ilable in practice: the overtaking distance of motorised vehicles and the 
 cyclist’s distance to the road edge or curb. These estimates are linked 
 with route position and video context\, allowing for comparisons of overta
 king behaviour during test rides before and after street space or infrastr
 ucture redesigns\, lane adjustments\, or other cycling-related interventio
 ns. \n\nThe core contribution is methodological: a reproducible workflow t
 hat translates everyday cycling trips into event-based evidence on safety 
 distances and overtaking conditions. Rather than relying solely on acciden
 t data\, aggregated traffic indicators\, or infrastructure classifications
 \, this approach measures the interaction itself. Additional event-based i
 ndicators include overtaking speed\, vehicle deceleration behaviour\, over
 taking duration\, and the spatial frequency of overtaking events. This pro
 vides a basis for assessing whether infrastructure changes have a measurab
 le impact on overtaking distance and behaviour. \n\nThe presentation outli
 nes the measurement pipeline\, the structure of the derived event data\, a
 nd the value of this approach for cycling research\, planning practice\, a
 nd the evidence-based evaluation of cycling infrastructure.
LOCATION:Plenary room (Lecture Room 2.2)
URL:https://pretalx.com/crbam-2026/talk/YSHXJB/
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