Solution: End-to-end framework based on VLM and generative planner
Traditional approaches rely heavily on rule engines and separate modules in tandem, leading to fragmented scene understanding.Orion's solution is organized into three key steps:
- Visual Language Model Integration: Align image features with linguistic commands in a 256-dimensional latent space by extracting long-term historical context (e.g., 10 consecutive seconds of driving frames) via the QT-Former module
- Multimodal Reasoning Architecture: A three-stage processing flow (sensing → reasoning → planning) is used, in which the LLM module supports parsing of scenes with very long text windows (up to 8192 tokens)
- Generative Trajectory Optimization: Generate 6 candidate trajectories using a diffusion model and filter the optimal paths through a differentiable kinematics layer
For specific implementations, developers should:
- Configure the reasoning_mode parameter in orion_stage3.py to be full
- Write natural language commands in commands.txt (e.g. watch out for merging vehicles on the right)
- Verify the inference using the eval_causal.py script
Empirical tests show that this scheme can improve the decision-making accuracy of complex intersections by 421 TP3T and reduce the false judgment rate to 1/3 of the traditional method.
This answer comes from the articleOrion: Xiaomi's Open Source End-to-End Autonomous Driving Reasoning and Planning FrameworkThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of insufficient causal inference in complex scenarios with traditional autopilot methods?
