Widespread development of driverless vehicles has led to the formation of autonomous racing, where technological development is accelerated by the high speeds and competitive environment of motorsport. A particular challenge for an autonomous vehicle is that of identifying a target trajectory - or, in the case of a competition vehicle, the racing line. Many existing approaches to finding the racing line are either not time-optimal solutions, or are computationally expensive - rendering them unsuitable for real-time application using on-board processing hardware. This study describes a machine learning approach to generating an accurate prediction of the racing line in real-time on desktop processing hardware. The proposed algorithm is a feed-forward neural network, trained using a dataset comprising racing lines for a large number of circuits calculated via traditional optimal control lap time simulation. The network predicts the racing line with a mean absolute error of +/-0.27m, and just +/-0.11m at corner apex - comparable to human drivers, and autonomous vehicle control subsystems. The approach generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal trajectory. Results suggest that for certain applications data-driven approaches to find near-optimal racing lines may be favourable to traditional computational methods.
翻译:无人驾驶车辆的广泛开发导致了自动赛车的形成,而这种赛车的技术发展加速了高速和机动车站竞争环境的高速和竞争性环境的加速。对自主车来说,一个特别的挑战就是确定目标轨迹,或者对竞争车而言,是赛车线。许多现有的寻找赛线的方法不是时间最佳的解决方案,或者计算上昂贵,使它们不适合使用机载处理硬件实时应用。本研究描述了一种机器学习方法,以便准确预测台式处理硬件的实时赛线。拟议的算法是一个进料前神经网络,培训使用一套数据集,其中包括通过传统最佳控制时间模拟计算的大量电路的赛线。网络预测赛线的绝对误差为+/0.27m,在车顶角仅+/0.11m,与人驾驶器和自主车辆控制子相近。该方法在33米内产生预测,比寻找最佳轨迹的传统方法要快9 000倍以上。结果表明,对于某些传统应用方法而言,可接近于有利的计算方法。