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首页> 外文会议>Unattended ground, sea, and air sensor technologies and applications XII >A Learning-Based Autonomous Driver: Emulate Human Driver's Intelligence in Low-speed Car Following
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A Learning-Based Autonomous Driver: Emulate Human Driver's Intelligence in Low-speed Car Following

机译：基于学习的自主驾驶员：在低速汽车中模仿人类驾驶员的智能

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In this paper, an offline learning mechanism based on the genetic algorithm is proposed for autonomous vehicles to emulate human driver behaviors. The autonomous driving ability is implemented based on a Prediction- and Cost function-Based algorithm (PCB). PCB is designed to emulate a human driver's decision process, which is modeled as traffic scenario prediction and evaluation. This paper focuses on using a learning algorithm to optimize PCB with very limited training data, so that PCB can have the ability to predict and evaluate traffic scenarios similarly to human drivers. 80 seconds of human driving data was collected in low-speed (< 30miles/h) car-following scenarios. In the low-speed car-following tests, PCB was able to perform more human-like car-following after learning. A more general 120 kilometer-long simulation showed that PCB performs robustly even in scenarios that are not part of the training set.
机译：本文提出了一种基于遗传算法的离线学习机制，用于自动驾驶汽车模拟驾驶员行为。自主驾驶能力是基于基于预测和成本函数的算法（PCB）来实现的。 PCB旨在模拟驾驶员的决策过程，该过程建模为交通场景的预测和评估。本文的重点是使用学习算法以非常有限的培训数据来优化PCB，以便PCB可以像人类驾驶员一样具有预测和评估交通状况的能力。在低速（<30英里/小时）的跟车场景中收集了80秒的人类驾驶数据。在低速汽车跟随测试中，PCB在学习后能够执行更像人类的汽车跟随。更为一般的120公里长的仿真表明，即使在不属于训练集的场景中，PCB也具有出色的性能。

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