论文标题
使用自我设计学习方向性软车道负担能力模型
Learning a Directional Soft Lane Affordance Model for Road Scenes Using Self-Supervision
论文作者
论文摘要
人类以有组织而灵活的方式在复杂的环境中浏览，适应上下文和隐性社会规则。了解这些自然学到的行为模式对于诸如自动驾驶汽车之类的应用至关重要。但是，从算法上定义这些隐含的人类行为规则仍然很困难。这项工作提出了一种新型的自我监督方法，用于训练概率网络模型，以估计人类最有可能驱动的地区以及每个点推断的旅行方向的多模式表示。该模型经过以驱动环境表示的条件为条件的单个人类轨迹。该模型被证明可以成功地概括为新的道路场景，这表明了现实世界应用的潜力是在挑战性或模棱两可的场景中具有社会可接受的驾驶行为的先验，而这些驾驶行为是由明确的交通规则处理不佳的。
Humans navigate complex environments in an organized yet flexible manner, adapting to the context and implicit social rules. Understanding these naturally learned patterns of behavior is essential for applications such as autonomous vehicles. However, algorithmically defining these implicit rules of human behavior remains difficult. This work proposes a novel self-supervised method for training a probabilistic network model to estimate the regions humans are most likely to drive in as well as a multimodal representation of the inferred direction of travel at each point. The model is trained on individual human trajectories conditioned on a representation of the driving environment. The model is shown to successfully generalize to new road scenes, demonstrating potential for real-world application as a prior for socially acceptable driving behavior in challenging or ambiguous scenarios which are poorly handled by explicit traffic rules.
