道路のルール：意味的相互作用の畳み込みモデルによる運転行動予測
Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions
高精度な3D知覚情報と詳細な意味地図を空間グリッド上で統合し、畳み込みモデルで車両や環境との相互作用を学習して将来の運転行動を予測する手法を提案し、新データセットで有効性を示した。
著者: Joey Hong, Benjamin Sapp, James Philbin
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
We focus on the problem of predicting future states of entities in complex, real-world driving scenarios. Previous research has used low-level signals to predict short time horizons, and has not addressed how to leverage key assets relied upon heavily by industry self-driving systems: (1) large 3D perception efforts which provide highly accurate 3D states of agents with rich attributes, and (2) detailed and accurate semantic maps of the environment (lanes, traffic lights, crosswalks, etc). We present a unified representation which encodes such high-level semantic information in a spatial grid, allowing the use of deep convolutional models to fuse complex scene context. This enables learning entity-entity and entity-environment interactions with simple, feed-forward computations in each timestep within an overall temporal model of an agent's behavior. We propose different ways of modelling the future as a distribution over future states using standard supervised learning. We introduce a novel dataset providing industry-grade rich perception and semantic inputs, and empirically show we can effectively learn fundamentals of driving behavior.
