The ability to anticipate pedestrian motion changes is a critical capability for autonomous vehicles. In urban environments, pedestrians may enter the road area and create a high risk for driving, and it is important to identify these cases. Typical predictors use the trajectory history to predict future motion, however in cases of motion initiation, motion in the trajectory may only be clearly visible after a delay, which can result in the pedestrian has entered the road area before an accurate prediction can be made. Appearance data includes useful information such as changes of gait, which are early indicators of motion changes, and can inform trajectory prediction. This work presents a comparative evaluation of trajectory-only and appearance-based methods for pedestrian prediction, and introduces a new dataset experiment for prediction using appearance. We create two trajectory and image datasets based on the combination of image and trajectory sequences from the popular NuScenes dataset, and examine prediction of trajectories using observed appearance to influence futures. This shows some advantages over trajectory prediction alone, although problems with the dataset prevent advantages of appearance-based models from being shown. We describe methods for improving the dataset and experiment to allow benefits of appearance-based models to be captured.

翻译：预测行人运动变化的能力是自动驾驶汽车的关键能力。在城市环境中，行人可能进入道路区域并引发驾驶高风险，识别这些情况至关重要。典型的预测器使用轨迹历史来预测未来运动，然而在运动启动的情况下，轨迹中的运动可能仅在一定延迟后才清晰可见，这可能导致行人已在做出准确预测之前进入道路区域。外观数据包含有用信息，例如步态变化，这些是运动变化的早期指标，并可为轨迹预测提供信息。本文对仅使用轨迹的方法和基于外观的方法进行了比较评估，并引入了一项新的基于外观预测的数据集实验。我们基于流行的NuScenes数据集中的图像和轨迹序列组合创建了两个轨迹与图像数据集，并研究了利用观测外观影响未来轨迹的预测方法。这表明相较于单独轨迹预测具有一定优势，尽管数据集的相关问题阻碍了基于外观模型的优势展现。我们描述了改进数据集和实验的方法，以充分捕捉基于外观模型带来的益处。