Collision Avoidance

Systems that detect nearby hazards and warn or steer buses, ships, or drones away from collisions.

Business impact

• avoidable collisions — Abellio London reported a 29% reduction in avoidable collisions.
• injuries resulting from avoidable collisions — Abellio London reported a 60% reduction in injuries from avoidable collisions.
• collision-free flights — MIT reported that SANDO avoided all moving obstacles across 12 real-drone flights.

Data requirements

• video from installed cameras (image) — Used in the bus and ship deployments to detect hazards.
• bridge navigation sensors (sensor) — Used in the ship deployment to support situational awareness.
• sonar (sensor) — Named input for the ship system.
• radar (sensor) — Named input for the ship system.
• GPS (sensor) — Named input for the ship system.
• AIS (tabular) — Named input for the ship system.

AI methods and techniques

• object detection — Used to identify hazards in real time for the ship deployment.
• machine learning — Named in the ship deployment as part of the real-time object-detection system.
• route planning — Used in the drone system to build safe corridors around moving hazards.
• sensor fusion — The ship deployment combines bridge sensors and cameras to support collision avoidance.
• warning systems — The bus deployment issues audio and visual warnings to the driver.

Industries

Real-world evidence

3 documented case studies on record.

Companies using this: Abellio London, CMA CGM, MIT.