Główne pojęcia
A deep learning-based approach that integrates prediction, decision, and planning modules to overcome the limitations of rule-based methods in real-world autonomous driving, especially for urban scenes, without relying on high-definition (HD) maps.
Streszczenie
The content presents a deep learning-based approach for autonomous driving that aims to overcome the limitations of traditional rule-based methods, which heavily rely on accurate prior knowledge such as HD maps. The proposed framework consists of two main components:
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Prediction Module:
- Utilizes a multi-stage design to predict scene-level occupancy grids and agent-level trajectories.
- The scene-level prediction leverages rasterized inputs to capture detailed environmental cues, while the agent-level prediction incorporates parametric landmark information and scene context.
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Planning Module:
- Employs a generator-evaluator paradigm to generate diverse trajectory candidates and evaluate them using a learned cost volume.
- The generator utilizes various sampling techniques, including curve-based, retrieval-based, and lattice-based samplers, to produce a set of candidate trajectories.
- The evaluator decodes a space-time cost map from the rasterized scene features and selects the optimal trajectory by minimizing the accumulated cost.
The key advantages of the proposed approach are:
- It eliminates the need for HD maps and manual planning rules, enabling efficient data utilization and adaptability to diverse driving scenarios.
- It maintains system interpretability and stability by decoupling perception from the end-to-end stack and integrating prediction and planning in a modularized manner.
- It is validated through extensive closed-loop testing in a complex, real-world urban environment using a factory-ready sensor set and compute platform, demonstrating the feasibility and commercial potential of the method.
Statystyki
The content does not provide specific numerical data or metrics. It focuses on describing the overall system design and the advantages of the proposed approach.
Cytaty
The content does not include any direct quotes.