In autonomous vehicles (AVs), early warning systems rely on collision prediction to ensure occupant safety. However, state-of-the-art methods using deep convolutional networks either fail at modeling collisions or are too expensive/slow, making them less suitable for deployment on AV edge hardware. To address these limitations, we propose sg2vec, a spatio-temporal scene-graph embedding methodology that uses Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) layers to predict future collisions via visual scene perception. We demonstrate that sg2vec predicts collisions 8.11% more accurately and 39.07% earlier than the state-of-the-art method on synthesized datasets, and 29.47% more accurately on a challenging real-world collision dataset. We also show that sg2vec is better than the state-of-the-art at transferring knowledge from synthetic datasets to real-world driving datasets. Finally, we demonstrate that sg2vec performs inference 9.3x faster with an 88.0% smaller model, 32.4% less power, and 92.8% less energy than the state-of-the-art method on the industry-standard Nvidia DRIVE PX 2 platform, making it more suitable for implementation on the edge.
翻译:在自主飞行器(AVs)中,早期预警系统依靠碰撞预测来确保居住者的安全。然而,使用深革命网络的最先进的方法,要么在模拟碰撞时失败,要么过于昂贵/低,使其不太适合用于AV边缘硬件。为了解决这些局限性,我们提议采用Sg2vec,即时空空间图像定位仪嵌入方法,利用图形神经网络(GNN)和长期短期内存(LSTM)层,通过视觉场景感知预测未来碰撞。我们表明,Sg2vec预测的碰撞率比合成数据集方面最先进的8.11%和39.07%更准确,比合成数据集方面最先进的方法要早,而29.47%更适用于具有挑战性的真实世界碰撞数据集。我们还表明,Sg2vec比从合成数据集(GNNNNN)和短期内存(LSTM)层预测未来碰撞率要好。我们证明,Sg2vec预测的是,以比88.0%的小型模型更快、32.4%的P-DR-DF 标准化平台上更适合的能量平台,92.8%和92。