One of the key challenges for autonomous vehicles is the ability to accurately predict the motion of other objects in the surrounding environment, such as pedestrians or other vehicles. In this contribution, a novel motion forecasting approach for autonomous vehicles is developed, inspired by the work of Gilles et al. [1]. We predict multiple heatmaps with a neuralnetwork-based model for every traffic participant in the vicinity of the autonomous vehicle; with one heatmap per timestep. The heatmaps are used as input to a novel sampling algorithm that extracts coordinates corresponding to the most likely future positions. We experiment with different encoders and decoders, as well as a comparison of two loss functions. Additionally, a new grid-scaling technique is introduced, showing further improved performance. Overall, our approach improves stateof-the-art miss rate performance for the function-relevant prediction interval of 3 seconds while being competitive in longer prediction intervals (up to eight seconds). The evaluation is done on the public 2022 Waymo motion challenge.

翻译：自主驾驶的一个关键挑战在于准确预测周围环境中其他物体（如行人或其他车辆）的运动能力。本研究基于Gilles等人[1]的工作，提出了一种新颖的自主驾驶运动预测方法。我们使用基于神经网络的模型为自主驾驶车辆附近每个交通参与者预测多个热力图，每个时间步对应一张热力图。这些热力图被输入到一种新颖的采样算法中，该算法提取与最可能未来位置对应的坐标。我们实验了不同的编码器和解码器，并比较了两种损失函数。此外，还引入了一种新的网格缩放技术，进一步提升了性能。总体而言，我们的方法在功能相关的3秒预测区间内改善了最先进水平的漏检率性能，同时在更长的预测区间（最长8秒）中具有竞争力。评估工作在2022年Waymo运动挑战赛公共数据集上进行。