Autonomous Driving Systems
Autonomous Driving Systems cover the perception, decision-making, and control functions that allow vehicles to operate with limited or no human intervention. These systems fuse sensor data, interpret the driving environment, plan safe maneuvers, and actuate steering, braking, and acceleration in real time. They are deployed across passenger cars, robotaxis, shuttles, and freight vehicles, with varying levels of autonomy from driver assistance to full self-driving. This application area matters because human error is a leading cause of road accidents and congestion. By automating driving tasks, organizations aim to improve safety, enable 24/7 mobility services, and unlock new business models such as robotaxi fleets and autonomous trucking. The AI stack here—spanning perception, localization, trajectory planning, and control—determines how reliably vehicles can navigate complex, dynamic environments and how quickly the industry can scale autonomous mobility at acceptable cost and risk.
The Problem
“Real-time perception-to-control stack for safe autonomous vehicle operation”
Organizations face these key challenges:
Edge-case failures in rare scenarios (construction zones, unusual vehicles, odd lighting/weather)
Sensor drift/misalignment causing unstable perception and inconsistent control
High false positives/negatives in detection leading to harsh braking or missed hazards