Idea
A trajectory prediction platform that denoises and predicts out-of-sight object paths for autonomous driving and robotics safety.
Research Paper
Core Innovation
This paper introduces a Vision-Positioning Denoising Module that uses camera calibration to create a mapping between vision and positioning data, enabling unsupervised denoising of noisy sensor trajectories. It extends out-of-sight trajectory prediction to both pedestrians and vehicles, addressing limitations of prior methods that assumed complete, noise-free observations. The approach achieves superior performance on benchmark datasets and integrates vision-positioning projection for the first time in this context.
Market Size (TAM)
$20–50B TAM for autonomous systems and robotics; $2–10B SAM from autonomous driving and surveillance industries. Driven by increasing demand for safety and reliable perception in complex environments.
Potential Customers & Pain Points
- Autonomous Vehicle Manufacturers Needing Reliable Out-of-Sight Object Tracking
- Robotics Companies Facing Sensor Noise and Occlusion Challenges
- Surveillance Systems Requiring Accurate Trajectory Prediction Despite Obstructions
- Virtual Reality Developers Seeking Realistic Agent Movement Prediction
Business Model
Licensing the denoising and prediction platform as an API to autonomous vehicle and robotics companies; offering custom integration and support services.
Competitive Landscape
- Waymo
- Tesla
- Mobileye
Implementation Challenges
- Integration with diverse sensor systems
- Real-time processing constraints
- Generalization to varied environments
Validation Strategy
- Benchmark against existing trajectory prediction and denoising methods on public datasets
- Pilot integration with autonomous vehicle sensor systems
- Collect real-world feedback to refine model robustness
Research Paper Overview
Out-of-Sight Trajectories: Tracking, Fusion, and Prediction
Summary
Trajectory prediction is critical for autonomous systems but existing methods struggle with out-of-sight objects and noisy sensor data. This work advances Out-of-Sight Trajectory Prediction by including pedestrians and vehicles, using a Vision-Positioning Denoising Module that leverages camera calibration to map vision and positioning data, denoising sensor inputs unsupervised. Evaluations on Vi-Fi and JRDB datasets show state-of-the-art denoising and prediction, outperforming baselines and traditional methods like Kalman filtering. This is the first approach integrating vision-positioning projection for denoising noisy trajectories of out-of-sight agents, enabling safer and more reliable predictions in real-world scenarios.