Autonomous driving in high-speed racing, as opposed to urban environments, presents significant challenges in scene understanding due to rapid changes in the track environment. Traditional sequential network approaches may struggle to meet the real-time knowledge and decision-making demands of an autonomous agent covering large displacements in a short time. This paper proposes a novel baseline architecture for developing sophisticated models capable of true hardware-enabled parallelism, achieving neural processing speeds that mirror the agent's high velocity. The proposed model (Parallel Perception Network (PPN)) consists of two independent neural networks, segmentation and reconstruction networks, running parallelly on separate accelerated hardware. The model takes raw 3D point cloud data from the LiDAR sensor as input and converts it into a 2D Bird's Eye View Map on both devices. Each network independently extracts its input features along space and time dimensions and produces outputs parallelly. The proposed method's model is trained on a system with two NVIDIA T4 GPUs, using a combination of loss functions, including edge preservation, and demonstrates a 2x speedup in model inference time compared to a sequential configuration. Implementation is available at: https://github.com/suwesh/Parallel-Perception-Network. Learned parameters of the trained networks are provided at: https://huggingface.co/suwesh/ParallelPerceptionNetwork.

翻译：与城市环境不同，高速赛车场景下的自动驾驶因赛道环境快速变化，在场景理解方面面临显著挑战。传统串行网络方法可能难以满足自主智能体在短时间内大范围移动时对实时感知与决策的需求。本文提出一种新颖的基线架构，用于开发能够实现真正硬件级并行化的复杂模型，其神经处理速度可与智能体的高速运动相匹配。所提出的并行感知网络模型包含两个独立神经网络——分割网络与重建网络，它们分别在独立的加速硬件上并行运行。该模型以激光雷达传感器采集的原始三维点云数据作为输入，并在两个设备上将其转换为二维鸟瞰图。每个网络沿空间与时间维度独立提取输入特征，并并行生成输出。所提方法的模型在配备双NVIDIA T4 GPU的系统上进行训练，采用结合边缘保持等多项损失函数的混合损失策略，实验表明其模型推理时间较串行配置实现了2倍加速。代码实现位于：https://github.com/suwesh/Parallel-Perception-Network。训练网络的已学习参数发布于：https://huggingface.co/suwesh/ParallelPerceptionNetwork。