Companies working on L2+, L3, and L4 automated vehicles drive thousands of miles collecting Petabytes of data. But only 5-10% of the data collected is useful for further R&D. The question is —which 5-10%? Nemo finds valuable events and traffic scenarios from driving logs.
Nemo finds events and traffic scenarios that match your search query from all of your data.
Think Google Search for your sensor data.
Provides developers instant access to the right data
Developers can zero-in the right data they need without spending their engineering time manually reviewing the driving logs. The shortlisted scenarios can be exported for algorithm training, product testing, annotation, or simulation.
Reduces storage cost by up to 85%
Ideally, only frequently accessed data should be kept in hot storage since it’s 10x costlier than cold storage. By automating the process of filtering and tiering the data based on which events and scenarios are useful for R&D teams, companies can save up to 85% in just the storage cost.
Provides scenario distribution and coverage analytics
Provides analytics such as heat maps, time-series plots, and frequency plots, that offer deeper insights to product development and testing teams in terms of scenario coverage, distribution, and biases in training/testing dataset.
Nemo scans the sensor data (coming from Lidars, camera, radars, and CAN bus) to find relevant metadata across objects in the scene, road infrastructure, traffic interactions between different objects, and the vehicle’s driving behavior.
Ego (self) Cars
Pedestrians
Bicyclist
Trucks
Emergency vehicles
Trailer
Motorcyclist
School bus
Highway Urban road
Flyover
Bridge
Intersection
4-way-stop
T-junction
Roundabout
Crosswalk
Cut-in Cut-out
Overtaking
Left/right turn
U-turn
On-coming traffic
Cross traffic
Parallel/anti-parallel
Hard brake Sudden acceleration
Speed value
Steering rate
Time to collision
Distance to front object
Lane departure
Rolling stop
Day Night
Rain
Snow
Fog
City/State
Geofence coordinates
Nemo scans the sensor data (coming from Lidars, camera, radars, and CAN bus) to find relevant metadata across objects in the scene, road infrastructure, traffic interactions between different objects, and the vehicle’s driving behavior.
Interested in learning more about our product or simply exploring ways to manage your growing data? We would love to help. Drop us a note for a private demo.