论文标题
CVAE-H：通过超网络和轨迹预测进行自动驾驶的轨迹预测
CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving
论文作者
论文摘要
在不同环境中预测道路代理的随机行为的任务是自主驾驶的一个挑战性问题。为了最好地理解场景环境并在不同环境中适应道路代理的未来状态，预测模型应是概率，多模式，上下文驱动和一般的。我们通过超网（CVAE-H）提出条件变异自动编码器；有条件的VAE广泛利用了超级net工作并针对高维问题（例如预测任务）执行生成任务。我们首先在简单的生成实验上评估CVAE-H，以表明CVAE-H是概率，多模式，上下文驱动和一般的。然后，我们证明了提出的模型通过在各种环境中对道路代理的准确预测有效地解决了自动驾驶预测问题。
The task of predicting stochastic behaviors of road agents in diverse environments is a challenging problem for autonomous driving. To best understand scene contexts and produce diverse possible future states of the road agents adaptively in different environments, a prediction model should be probabilistic, multi-modal, context-driven, and general. We present Conditionalizing Variational AutoEncoders via Hypernetworks (CVAE-H); a conditional VAE that extensively leverages hypernetwork and performs generative tasks for high-dimensional problems like the prediction task. We first evaluate CVAE-H on simple generative experiments to show that CVAE-H is probabilistic, multi-modal, context-driven, and general. Then, we demonstrate that the proposed model effectively solves a self-driving prediction problem by producing accurate predictions of road agents in various environments.
