Predicting future trajectories of traffic agents in highly interactive environments is an essential and challenging problem for the safe operation of autonomous driving systems. On the basis of the fact that self-driving vehicles are equipped with various types of sensors (e.g., LiDAR scanner, RGB camera, radar, etc.), we propose a Cross-Modal Embedding framework that aims to benefit from the use of multiple input modalities. At training time, our model learns to embed a set of complementary features in a shared latent space by jointly optimizing the objective functions across different types of input data. At test time, a single input modality (e.g., LiDAR data) is required to generate predictions from the input perspective (i.e., in the LiDAR space), while taking advantages from the model trained with multiple sensor modalities. An extensive evaluation is conducted to show the efficacy of the proposed framework using two benchmark driving datasets.
翻译:预测在高度互动环境中的交通代理器未来轨迹是自主驾驶系统安全运行的一个至关重要和具有挑战性的问题,基于自驾驶车辆配备了各种传感器(如利达雷达扫描仪、RGB照相机、雷达等)这一事实,我们提议了一个跨模式嵌入框架,目的是从多种输入模式的使用中受益。在培训时间,我们的模型学会将一套互补特征嵌入共享的潜在空间,共同优化不同类型输入数据的目标功能。在测试时间,需要一种单一输入模式(如利达雷达数据),以便从输入角度(即利达雷达空间)作出预测,同时利用经过多种传感器模式培训的模型,利用两个基准驱动数据集进行广泛评估,以显示拟议框架的有效性。