Motion Prediction of Surrounding Vehicles in Highway Scenarios With Deep Learning
Abstract: Anticipating the future positions of the surrounding vehicles is a crucial task foran autonomous vehicle in order to drive safely. To foresee complex manoeuvresfor longer time horizons, a framework that relies on high-level properties ofmotion and is able to incorporate, e.g. contextual features, is needed. In thisthesis, the problem of predicting the trajectories of the surrounding vehicles ona highway is tackled by using machine learning. The objective is to evaluate theperformance of recurrent neural networks for trajectory prediction, specificallylong-short term memory neural networks. Moreover, the goal is to investigateif contextual features can improve the predictions.The problem of predicting future trajectories is solved by using two differentapproaches, which are compared by using the same framework. The firstapproach is based on the vehicle states of the surrounding vehicles relative tothe ego-vehicle, where the reference system is in the ego-vehicle. The secondapproach is based on the velocities of the vehicles relative to the ground, wherethe reference system is in the ground. The results show that, with the proposedarchitecture, the latter approach results in a lower RMSE in the longitudinaldirection compared with the former approach. The results also show that theproposed models, overall, outperform a simple model, which is based on polynomialfitting, particularly in the lateral direction where the proposed modelsare significantly better than the polynomial models. Furthermore, contextualfeatures do not improve the predictions significantly. However, the results indicatethat contextual information has a positive impact on the predictions inspecific scenarios.
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