论文标题
车道辅助损失的自动驾驶中的轨迹预测
Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss
论文作者
论文摘要
预测车辆的轨迹是自动驾驶汽车在复杂的城市交通场景中导航的重要能力。 Bird's-eye-eview路线图信息提供了进行轨迹预测的有价值的信息，而最先进的模型通过图像卷积提取此信息，而辅助损失功能可以通过进一步编码社交和法律驾驶行为的常识来增强从深度学习中推断出的模式。由于人类的驾驶行为本质上是多模式，因此允许多模式输出的模型倾向于在标准指标上优于单一预测模型。我们提出了一个损失函数，该功能通过在所有预测模式上执行预期的驾驶规则来增强此类模型。我们对轨迹预测的贡献是双重的。我们提出了一个新的度量标准，该指标通过惩罚反对驾驶道的归因标准（流动方向）的轨迹来解决越野率指标的故障案例，我们表明该指标是可区分的，因此适用于辅助损失函数。然后，我们使用这种辅助损失来扩展标准的多重轨迹预测（MTP）和多径模型，从而通过预测轨迹更好地符合道路的车道遵循规则，从而在Nuscenes预测基准上取得了改进的结果。
Predicting a vehicle's trajectory is an essential ability for autonomous vehicles navigating through complex urban traffic scenes. Bird's-eye-view roadmap information provides valuable information for making trajectory predictions, and while state-of-the-art models extract this information via image convolution, auxiliary loss functions can augment patterns inferred from deep learning by further encoding common knowledge of social and legal driving behaviors. Since human driving behavior is inherently multimodal, models which allow for multimodal output tend to outperform single-prediction models on standard metrics. We propose a loss function which enhances such models by enforcing expected driving rules on all predicted modes. Our contribution to trajectory prediction is twofold; we propose a new metric which addresses failure cases of the off-road rate metric by penalizing trajectories that oppose the ascribed heading (flow direction) of a driving lane, and we show this metric to be differentiable and therefore suitable as an auxiliary loss function. We then use this auxiliary loss to extend the the standard multiple trajectory prediction (MTP) and MultiPath models, achieving improved results on the nuScenes prediction benchmark by predicting trajectories which better conform to the lane-following rules of the road.
