Reliably predicting the motion of contestant vehicles surrounding an autonomous racecar is crucial for effective and performant planning. Although highly expressive, deep neural networks are black-box models, making their usage challenging in safety-critical applications, such as autonomous driving. In this paper, we introduce a structured way of forecasting the movement of opposing racecars with deep neural networks. The resulting set of possible output trajectories is constrained. Hence quality guarantees about the prediction can be given. We report the performance of the model by evaluating it together with an LSTM-based encoder-decoder architecture on data acquired from high-fidelity Hardware-in-the-Loop simulations. The proposed approach outperforms the baseline regarding the prediction accuracy but still fulfills the quality guarantees. Thus, a robust real-world application of the model is proven. The presented model was deployed on the racecar of the Technical University of Munich for the Indy Autonomous Challenge 2021. The code used in this research is available as open-source software at www.github.com/TUMFTM/MixNet.
翻译:虽然深度神经网络是黑盒模型,因此在安全关键应用方面使用起来具有挑战性,例如自主驾驶。在本文件中,我们采用了一种结构化的方法来预测具有深层神经网络的对立赛车的移动情况,由此形成的一套可能的输出轨迹受到限制。因此,可以提供预测的质量保障。我们通过评价模型的性能,与基于LSTM的编码器-分解器结构一起报告模型的性能,该结构涉及从高纤维硬件在Loop模拟中获取的数据。提议的方法超过了预测准确性的基准,但仍符合质量保障。因此,该模型的可靠真实应用得到了证明。该模型是在慕尼黑技术大学Indy自动挑战2021号的竞赛车上部署的。该研究所使用的代码作为开放源软件可在www.github.com/TUMFTM/MixNet上查阅。