Difference between revisions of "Projects:2019s1-124 Development of a Tool for Naturalistic Measurement of Vehicle-Cyclist Passing Distances"

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Jamie Mackenzie (CASR)
 
Jamie Mackenzie (CASR)
  
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Motivation:
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== Motivation ==
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Cycling is something to be supported as it can have health and environmental benefits amongst others. Cycling leaves a commuter more vulnerable than other road users and though rules exist to protect them, the infrastructure for cycling lanes and paths could be improved to further protect cyclists. Research has shown that cars overtaking cyclists too closely results in more severe injury than other forms of cyclist/car crashes so minimising the chance of a dangerous close overtakes is of great value. Little is known about real-world car/cyclist dynamic interactions and learning more about this would be of value to making decisions regarding infrastructure and legislation.
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While cycling is a mode of transport to be encouraged, cyclists are far more likely to be killed or hospitalised from road accidents than other commuters [1]. Having accurate data on where, how often, and how close cars overtake cyclists is an important step towards improving the laws and infrastructure that keep cyclists safe.
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== Aim ==
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This project is sponsored by the Centre for Automotive Safety Research (CASR) and seeks to improve and validate the capabilities of an existing bicycle mounted device for measuring vehicle-cyclist passing distances. The three areas of focus are the sensor choice, device design and verification.
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== Sensor Choice ==
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<br>
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'''Existing Limitations:'''<br> Measurement Frequency and Size<br>
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'''Sensor considerations:'''<br>
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Measurement Frequency<br>
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Resolution/Accuracy<br>
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Cost<br>
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Durability<br>
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Power Consumption<br>
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Size<br>
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Weight<br>
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Communication<br>
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Interference<br>
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Other Output Data<br>
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'''Available technologies and sensors:'''<br>
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Ultrasonic and LiDAR
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=== Conclusion: ===
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<br>
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On-road testing results showed both LiDAR sensors were able to detect 38% more overtakes than the ultrasonic sensor. A particular  LiDAR sensor was chosen as it is not limited by frequency and maintained accuracy in all lighting conditions. This will allow the sensor housings to be considerably smaller whilst maintaining durability and detecting more overtakes.
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== Verification ==
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Verifying the accuracy of the data collected adds value to existing and all future trial results.
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The steps taken to verify the device are as follows:<br>
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* Mount a Raspberry Pi camera to the bicycle<br>
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* Sync the camera with the sensor data<br>
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* Analyse the video and accuracy of the sensors<br>
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* Build and implement a detection algorithm<br>
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'''Conclusion:'''<br> Testing revealed video recordings as costly and time consuming method, but allowed for more efficient sensor detection algorithms to be developed.

Revision as of 15:37, 30 October 2019

Members: Scott Adamson Robert Broadhead Max Telford

Supervisors: Braden Phillips Jamie Mackenzie (CASR)

Motivation

While cycling is a mode of transport to be encouraged, cyclists are far more likely to be killed or hospitalised from road accidents than other commuters [1]. Having accurate data on where, how often, and how close cars overtake cyclists is an important step towards improving the laws and infrastructure that keep cyclists safe.

Aim

This project is sponsored by the Centre for Automotive Safety Research (CASR) and seeks to improve and validate the capabilities of an existing bicycle mounted device for measuring vehicle-cyclist passing distances. The three areas of focus are the sensor choice, device design and verification.


Sensor Choice


Existing Limitations:
Measurement Frequency and Size
Sensor considerations:
Measurement Frequency
Resolution/Accuracy
Cost
Durability
Power Consumption
Size
Weight
Communication
Interference
Other Output Data


Available technologies and sensors:
Ultrasonic and LiDAR

Conclusion:


On-road testing results showed both LiDAR sensors were able to detect 38% more overtakes than the ultrasonic sensor. A particular  LiDAR sensor was chosen as it is not limited by frequency and maintained accuracy in all lighting conditions. This will allow the sensor housings to be considerably smaller whilst maintaining durability and detecting more overtakes.



Verification

Verifying the accuracy of the data collected adds value to existing and all future trial results. The steps taken to verify the device are as follows:

  • Mount a Raspberry Pi camera to the bicycle
  • Sync the camera with the sensor data
  • Analyse the video and accuracy of the sensors
  • Build and implement a detection algorithm

Conclusion:
 Testing revealed video recordings as costly and time consuming method, but allowed for more efficient sensor detection algorithms to be developed.

Retrieved from "https://projectswiki.eleceng.adelaide.edu.au/projects/index.php?title=Projects:2019s1-124_Development_of_a_Tool_for_Naturalistic_Measurement_of_Vehicle-Cyclist_Passing_Distances&oldid=13808"