论文标题
神经进化中的应用在自动驾驶汽车中
Application of Neuroevolution in Autonomous Cars
论文作者
论文摘要
随着电动汽车的发作，它们变得越来越受欢迎，自动驾驶汽车是旅行/驾驶体验的未来。达到5级自主权的障碍是数据收集的困难，该数据融合了良好的驾驶习惯及其缺乏。当前实施自动驾驶汽车的问题是需要大量的数据集，并且需要评估数据集中的驾驶。我们提出了一个不需要数据培训数据的系统。进化模型将具有针对健身函数的优化能力。我们已经实施了神经进化，一种遗传算法的一种形式，可以在模拟的虚拟环境中训练/进化的自动驾驶汽车，借助虚幻引擎4，利用Nvidia的Physx Physics Engine来准确地描绘现实世界中的车辆动力学。我们能够观察进化的偶然性，并利用它来达到我们的最佳解决方案。我们还证明了遗传算法带来的概括属性的易于概括，以及如何将它们用作样板，可以使用其他机器学习技术来改善整体驾驶体验。
With the onset of Electric vehicles, and them becoming more and more popular, autonomous cars are the future in the travel/driving experience. The barrier to reaching level 5 autonomy is the difficulty in the collection of data that incorporates good driving habits and the lack thereof. The problem with current implementations of self-driving cars is the need for massively large datasets and the need to evaluate the driving in the dataset. We propose a system that requires no data for its training. An evolutionary model would have the capability to optimize itself towards the fitness function. We have implemented Neuroevolution, a form of genetic algorithm, to train/evolve self-driving cars in a simulated virtual environment with the help of Unreal Engine 4, which utilizes Nvidia's PhysX Physics Engine to portray real-world vehicle dynamics accurately. We were able to observe the serendipitous nature of evolution and have exploited it to reach our optimal solution. We also demonstrate the ease in generalizing attributes brought about by genetic algorithms and how they may be used as a boilerplate upon which other machine learning techniques may be used to improve the overall driving experience.
