论文标题
平衡数据集的重要性：基于神经网络和分布式表示形式分析车辆轨迹预测模型
The Importance of Balanced Data Sets: Analyzing a Vehicle Trajectory Prediction Model based on Neural Networks and Distributed Representations
论文作者
论文摘要
预测其他交通参与者的未来行为是一项必不可少的任务，需要由自动化的车辆和人类驾驶员来解决，以实现安全和局势驾驶。车辆轨迹预测的现代方法通常依赖于数据驱动的模型，例如神经网络，尤其是LSTMS（长期短期内存），从而实现了令人鼓舞的结果。但是，基础培训数据最佳组成的问题受到了较少的关注。在本文中，我们根据基于神经网络模型的车辆轨迹预测进行了扩展，采用分布式表示形式来编码语义矢量基板中的汽车场景。我们分析了培训数据中的变化对预测模型性能的影响。因此，我们表明，使用我们的语义矢量表示的模型在对足够数据集进行训练时，超出数值模型，从而优于数值模型，从而，车辆轨迹预测中训练数据的组成对于成功培训至关重要。我们对挑战现实驾驶数据的分析进行分析。
Predicting future behavior of other traffic participants is an essential task that needs to be solved by automated vehicles and human drivers alike to achieve safe and situationaware driving. Modern approaches to vehicles trajectory prediction typically rely on data-driven models like neural networks, in particular LSTMs (Long Short-Term Memorys), achieving promising results. However, the question of optimal composition of the underlying training data has received less attention. In this paper, we expand on previous work on vehicle trajectory prediction based on neural network models employing distributed representations to encode automotive scenes in a semantic vector substrate. We analyze the influence of variations in the training data on the performance of our prediction models. Thereby, we show that the models employing our semantic vector representation outperform the numerical model when trained on an adequate data set and thereby, that the composition of training data in vehicle trajectory prediction is crucial for successful training. We conduct our analysis on challenging real-world driving data.
