AutoDrive: Autonomous vehicle navigation with advanced Neural Networks

Challenge

The client needed a high-precision navigation system for their autonomous delivery fleet. Existing models struggled with real-world variability — including low-light conditions, complex intersections, and inconsistent road signage — putting safety, reliability, and scalability at risk.

Solution

Seawolf AI partnered with the client’s engineering team to develop AutoDrive, a multi-modal navigation stack powered by neural networks trained for high-variance urban environments.

Key components included:

• Sensor fusion modeling for real-time interpretation of LIDAR, camera, and GPS inputs

• Reinforcement learning frameworks to continuously adapt to unseen road conditions

• Edge-deployable inference models with hardware optimization for power-constrained vehicles

• Agentic coordination across vehicle fleets for collective learning and path optimization

Results

✅ 65% reduction in navigation errors on complex routes
✅ 22% increase in delivery speed without human intervention
✅ Fleet-wide system updates and performance sharing via autonomous agents
✅ Scalable deployment from urban to suburban terrains

What Made It Different

AutoDrive wasn’t just AI in the loop — it was AI at the wheel. We architected an adaptive, self-learning navigation system capable of operating independently while learning collectively.

“Seawolf brought the engineering rigor we needed to cross the autonomy threshold.”
— Head of Autonomy, Client (Mobility Tech)