A collision avoidance system is a safety system designed to warn, alert, or assist drivers to avoid imminent collisions and reduce the risk of incidents. Collision avoidance systems use a variety of technologies and sensors, such as radar, lasers, cameras, GPS, and artificial intelligence. Not all collision avoidance systems are created equally—some warn or alert, while others override the driver to assist them in avoiding collisions and mitigating risk.
Types of Collision Avoidance Alert Systems:
- Forward Collision Warning (FCW)
- Blind-spot Warning (BSW)
- Lane Departure Warning (LDW)
- Cross Traffic Warning
- Pedestrian Detection System
Types of Collision Avoidance Assist Systems:
- Automatic Emergency Braking (AEB)
- Adaptive Cruise Control
- Electronic Stability Control (ESC)
- Parking Assist
Why do commercial fleets need collision avoidance systems?
Fleet managers and safety leaders know their greatest resource is their drivers—their ultimate goal is to keep driver’s safe, successful, and feeling valued. However, they also understand how critical it is to keep vehicles and drivers productive on the road, reduce collisions, and decrease liability and maintenance costs. When struggling to balance this, many turn to in-vehicle collision avoidance systems to help reduce collisions and costs and keep their driver’s safe.
How is AI evolving collision avoidance systems?
The use of AI sensors in the vehicle to detect driver movement, gaze direction/attention, vehicle activity, traffic conditions, and other contextual data to make real-time decisions about imminent risks can provide on average a 40%-60% reduction in collision frequency and collision-related costs. AI sensors and real-time AI intervention in the vehicle with 4 out of 5 drivers reduce distracted driving WITHOUT manager involvement. AI safety systems do not require human review or video access at all and are perfectly compatible with ensuring driver privacy.
How Nauto Predictive Collision Alerts work
Reducing fleet collisions requires more than safety policies, traditional driver training, and physics-based ADAS systems. Nauto Predictive Collision Alerts are the first in the industry to simultaneously fuse critical inputs, including driver behavior, to provide drivers with enough time to react and prevent imminent collisions in real-time, before they happen, not after.
Nauto’s collision avoidance system dual-facing cameras installed in the vehicle’s cabin use computer vision (a form of AI) and proprietary algorithms to assess the situational risk and determine the severity of the distraction, as well as understand potential risks on the road ahead, such as tailgating or nearly running traffic lights and stop signs. The Nauto camera continuously synthesizes inputs from in and around the vehicle, including driver behavior, vehicle movement, traffic elements, and contextual data, in its multi-tasked Convolutional Neural Networks (CNN) model to determine levels of collision risk. As the detected risk intensifies, Predictive Collision Alerts signals the driver to take action with increasing levels of urgent alerts. Predictive Collision Alerts could give drivers as much as 100 extra feet to react to a potential collision when traveling at 60 mph.