SLHP: Short-/Long-term Hybrid Prediction for Road Users

We propose a novel method called Short-/Long-term Hybrid Prediction (SLHP) that predicts short-term and long-term trajectories of surrounding objects while estimating their future influences on an autonomous ego-vehicle for both types of trajectories. Recently, long-term prediction methods based on trajectory sample generation and verification with road environments and/or interactions have been proposed; however, they entail high computational costs because they need to generate and verify multiple trajectory samples for multiple objects. Therefore, they are not appropriate in scenes where short-term prediction is required such as for sudden motions by surrounding objects. In contrast, our SLHP consists of a hybrid prediction based on short-term and long-term trajectory predictors. SLHP provides flexible predictions that are appropriate to scenes caused by objects' motions, road environments, interactions, and so on. In this paper, we apply and evaluate our method to cut-in prediction as a typical prediction task by using the public road dataset that includes various cut-in events. Experimental results show that SLHP achieves a correctness rate of F-measure = 0.86 for cut-in prediction. Additionally, we confirmed the effectiveness of our hybrid prediction method that provides prediction as early as 3.57 s and 4.82 s before the cut-in event for short-term and long-term trajectory predictions, respectively.