Another glance under the hood of CAT™ - Collision Aversion Technology and why a Predictive Vision for Motorcycles.

Just a few weeks ago, I enjoyed discussing Ride Vision with Alex Tilkin. Following this discussion, I had a pleasure to further address a few questions from the community of Automotive engineers.
Indeed, the problem that Ride Vision solves is unique and intriguing. Preventing and reducing motorbike accidents demands peculiar approach, specific to the domain of 2-wheelers.
Besides the intrinsic difficulties that the 2-wheelers impose on the proposed solution (which you can read in Alex’s article), one of the other enormous challenges is the Threat Analysis. Threat Analysis model at Ride Vision is responsible in understanding the actual threats/hazards imposed on the motorcycle/motorcyclist and raise the alert accordingly.
Any Collision Avoidance system for motorcycles has to take into the account the challenging and very dynamic maneuver of such a vehicle on the roads (this is in addition to the actual bumpy and agile running of a motorcycle itself, that Alex covered in his article). To emphasize this point, let me share with you a short footage (recorded by one of the Ride Vision’s systems) representing this dynamic nature.
I’m sure that if you don’t ride you’ve probably shook your head a few times. But in case you still find that this doesn’t matter and the solution is somewhat similar in behavior to cars, you’re welcomed to review how this looks at the a bit extreme case.
This is where the power of Predictive Vision comes handy. You see, even in the madness (of this last video) one can spot some patterns. Patterns of the 2-wheelers road behavior that can be inferred and blended with a threat analysis of detected objects around the motorbike and together form the required solution to assess threats on the motorcycles.
This is exactly how the Ride Vision’s CAT™ works.
The surrounding is being constantly analyzed, detected and tracked. The patterns of the motorcycle behaviors on the road are being learned and inferred and the Threat Analysis constantly blends and fuses all the data points to infer dynamically whether any threat can emerge.
Challenging? Yes, but very rewarding…
Riding a bike equipped with a Ride Vision’s system only emphasizes the importance of extending the right safety tools at the hands of riders. An extra, precious time to react to evolving situations is priceless…
Our lives depend on it.
