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| | license: osl-3.0 |
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| | Abstract: |
| | Autonomous driving when applied for high-speed racing aside from urban environments |
| | presents challenges in scene understanding due to rapid changes in the track environment. |
| | Traditional sequential network approaches might struggle to keep up with the real-time |
| | knowledge and decision-making demands of an autonomous agent which covers large |
| | displacements in a short time. This paper proposes a novel baseline architecture for |
| | developing sophisticated models with the ability of true hardware-enabled parallelism |
| | to achieve neural processing speeds to mirror the agent’s high velocity. The proposed |
| | model, named Parallel Perception Network (PPN) consists of two independent neural |
| | networks, a segmentation and a reconstruction network running in parallel on separate |
| | accelerated hardware. The model takes raw 3D point cloud data from the LiDAR sensor as |
| | input and converts them into a 2D Bird’s Eye View Map on both devices. Each network |
| | extracts its input features along space and time dimensions independently and produces |
| | outputs in parallel. Our model is trained on a system with 2 NVIDIA T4 GPUs with a |
| | combination of loss functions including edge preservation, and shows a 1.8x speed up in |
| | model inference time compared to a sequential configuration. Implementation is available |
| | at: https://github.com/suwesh/Parallel-Perception-Network. |
