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Jiang VAD Vectorized Scene Representation For Efficient Autonomous Driving ICCV 2023 Paper by dhanyaksaval is a document available to read on EtoBox.

What is Jiang VAD Vectorized Scene Representation For Efficient Autonomous Driving ICCV 2023 Paper about?

The paper presents VAD, a novel end-to-end vectorized paradigm for autonomous driving that enhances trajectory planning by utilizing a fully vectorized scene representation, improving both safety and efficiency. VAD significantly outperforms traditional rasterized methods by reducing collision rates and increasing planning speed, achieving state-of-the-art performance on the nuScenes dataset. The framework incorporates instance-level planning constraints to optimize the planning process without the computat

Author

dhanyaksaval

Language

EN