ML algorithms, battery-efficient dashcam kit address parked vehicle monitoring

Aspinity has introduced a suite of new analogML™ algorithms tailored for parked-vehicle monitoring. These algorithms enable continuous vehicle monitoring without compromising battery life, addressing a critical challenge faced by the automotive industry.

Additionally, the company has unveiled a dashcam-focused evaluation kit designed to enable precise detection and recording of security events over extended periods without draining the vehicle’s battery or necessitating an external power source. The kit features Aspinity’s AML100-REF-1 wireless, battery-operated evaluation module, facilitating quick deployment and evaluation within the vehicle cabin.

Aspinity’s new automotive evaluation kit boasts superior event detection accuracy compared to conventional g-sensor-based solutions. The company’s acoustic-based trigger system and analogML algorithms accurately detect relevant events such as door handle movement, neighboring car impacts, and glass breakage, while disregarding irrelevant sounds that often trigger g-sensors.

Utilizing the sensor fusion capabilities of the AML100, Aspinity’s comprehensive library of automotive surveillance algorithms leverages analog input signals from various sensors, including acoustic, piezoelectric, and radar sensors. This allows for customization based on the monitoring application, sensitivity requirements, and sensor placement within the vehicle, providing electronics engineers with a flexible and powerful solution for parked vehicle monitoring.