Map-Adaptive Multimodal Trajectory Prediction via Intention-Aware Unimodal Trajectory Predictors

Autonomous vehicles necessitate prediction of future motions of surrounding traffic participants for safe navigation. However, prediction is challenging due to complex road structures and the multimodality of driving behaviors. Recent approaches usually output a fixed number of predicted trajectories, thus can hardly generalize to situations with more options and match modalities in different situations. They also rely on complex loss design and time-consuming training. This work proposes a novel map-adaptive multimodal trajectory predictor that links driving modalities, driver's intentions, and a vehicle's candidate centerlines (CCLs) together, rendering the predictor map-adaptive and the multimodality explainable. The predictor is derived by training an intention-aware unimodal trajectory predictor, which consists of a CCL-based goal predictor and a goal-directed trajectory completer, and a CCL scorer for estimating the possibilities of a target vehicle (TV) choosing a CCL to follow. This decomposed approach simplifies the training process and reduces the computational resources required, thereby rendering it a faster and more cost-effective alternative. Additionally, the proposed predictor has demonstrated comparable or even superior performance to traditional multimodal predictors in specific applications. Overall, the unimodal predictor presents a promising approach for practical machine learning applications, particularly when computational resources are limited.