Idea
Camera-based perception and planning platform enabling autonomous vehicles to detect and avoid obstacles for safer navigation.
Research Paper
Core Innovation
This paper introduces a camera-only perception system that combines YOLOv11 object detection with monocular depth estimation models like Depth Anything V2. It uniquely integrates these with a Frenet-Pure Pursuit planning strategy to enable robust obstacle avoidance without relying on expensive sensors. This approach improves cost-efficiency and adaptability in real-world autonomous navigation scenarios.
Market Size (TAM)
$20–50B TAM, $2–10B SAM; assumption: Autonomous vehicle and robotics markets growing with demand for cost-effective perception systems.
Potential Customers & Pain Points
- Autonomous Vehicle Manufacturers needing reliable obstacle avoidance
- Robotics Companies requiring efficient vision-based navigation
- Urban Mobility Services seeking safer self-driving solutions
Business Model
Licensing the perception and planning software platform to autonomous vehicle OEMs and robotics companies; offering integration and support services.
Competitive Landscape
- Tesla Autopilot
- Waymo
- Mobileye
Implementation Challenges
- Monocular depth estimation accuracy under diverse conditions
- Integration complexity with existing vehicle control systems
- Real-time processing constraints on embedded hardware
Validation Strategy
- Pilot integration with autonomous vehicle prototypes
- Field testing in diverse urban and highway environments
- Performance benchmarking against multi-sensor systems
Research Paper Overview
Vision-based Perception for Autonomous Vehicles in Obstacle Avoidance Scenarios
Summary
This paper presents an efficient obstacle avoidance system for autonomous vehicles using a camera-only perception module combined with a Frenet-Pure Pursuit planning strategy. It integrates YOLOv11 for object detection and advanced monocular depth estimation models like Depth Anything V2 to estimate distances. The system is tested in diverse real-world scenarios, demonstrating robust and accurate navigation around obstacles.