1. The Cyber-Physical Bike A Step Toward Safer Green Transportation

2. Dangers of Cycling ✤ In the US over 700 bicyclists die each year in accidents with automobiles ✤ 44,000 annual reported cases of injuries due to bicycle-automobile accidents ✤ Accidents are considerably more dangerous for cyclists

3. Dangers of Cycling ✤ Bikers forfeit a large amount of cognitive and physical capabilities on situational awareness. ✤ Must continually check for approaching vehicles, forcing the biker to take attention away from the forward situation as well as operating the bycicle. ✤ A preventive biker-centric solution is needed.

4. The Solution: Cyber-Physical ✤ Enhance traditional bike with sensing and computational capabilities. ✤ Computer vision and mobile computing. ✤ Provide accurate and timely detection of rear-approaching vehicles alerting the biker of the pending encounter

5. Current Solutions ✤ Rear View Mirror ✤ Inconsistent viewing angles ✤ Still require visual attention from the rider ✤ Digital Rear View ✤ Requires rider’s visual attention even in absence of cars

6. Current Solutions ✤ Infrastructure: Bike lanes can help, but must be extensive and require considerable public commitment to build and maintain. ✤ Biker Visibility ✤ Reflectors, vests, flashing lights are preventive but do not provide awareness to the biker. ✤ Legislation: ✤ Most laws deal with accidents after the fact. ✤ Little can be done to prevent collisions.

7. Cyber-Physical: Challenges ✤ Limited power generation ✤ Weight restrictions ✤ Unstable platform ✤ Difficult for reliable video feed. ✤ Differentiating vehicle direction ✤ Rear vs. Front approaches

8. Possible Technologies

9. The Cyber-Physical Bike

10. Features ✤ Rear facing camera ✤ Produces video feed to computer ✤ On board computer ✤ Constantly processing video feed for approaching vehicles. ✤ Monitors virtual safety zone

11. Video Processing ✤ Optical Flow ✤ Optical flow (OF) is the pattern of apparent motion of objects in a scene caused by the relative motion between an observer and the objects ✤ Differentiates between the direction of moving objects in a seen. ✤ Provides stark contrast between approaching and leaving vehicles.

12. Optical Flow Analysis

13. Roadway Segmentation ✤ Uses the features of the road to divide the FOV into necessary and unnecessary parts. ✤ “To facilitate this, a fixed region in the bottom part of the image, which always contains the road, is chosen to train and update the model for every frame. Then, this roadway color model is utilized to segment the rest of the scene.” ✤ Edge detection uses line-like features and lane markers to determine the vanishing point of the scene. ✤ Reduces processing time for each image.

14. Vehicle Tracking ✤ A detected approaching vehicle is tracked while it remains in FOV ✤ Provides a second alert for a dangerous approach.

15. Accuracy and Performance -Relatively stable performance. -73% Accuracy for OF -3 FPS while analyzing

16. Accuracy and Performance -Large difference between vehicle and no vehicle. -3.6 FPS while analyzing without -1.7 FPS while tracking approach

17. Overview ✤ Processing runs around 3.5 FPS ✤ Can provide an alert 3.5 seconds before an encounter. ✤ Estimated 5 hour battery life.