An important topic in the autonomous driving research is the development of maneuver planning systems. Vehicles have to interact and negotiate with each other so that optimal choices, in terms of time and safety, are taken. For this purpose, we present a maneuver planning module able to negotiate the entering in busy roundabouts. The proposed module is based on a neural network trained to predict when and how entering the roundabout throughout the whole duration of the maneuver. Our model is trained with a novel implementation of A3C, which we will call Delayed A3C (D-A3C), in a synthetic environment where vehicles move in a realistic manner with interaction capabilities. In addition, the system is trained such that agents feature a unique tunable behavior, emulating real world scenarios where drivers have their own driving styles. Similarly, the maneuver can be performed using different aggressiveness levels, which is particularly useful to manage busy scenarios where conservative rule-based policies would result in undefined waits.
翻译:自主驱动研究的一个重要议题是开发机动规划系统。 车辆必须相互交流和谈判,以便从时间和安全角度作出最佳选择。 为此,我们提出了一个机动规划模块,能够谈判繁忙的圆环路进入。 拟议的模块基于一个神经网络,经过培训,可以预测在整个机动过程中何时和如何进入圆环路。 我们的模型经过新颖的A3C执行培训,我们将在汽车以现实方式与互动能力流动的合成环境中称之为延迟A3C(D-A3C),在这种合成环境中,车辆以现实的方式移动。 此外,该系统还经过培训,使代理人具有独特的金枪鱼行为特征,模拟司机有自己驾驶风格的现实世界情景。 同样,这种操作也可以使用不同的攻击性水平来进行,这对于管理繁忙的情景特别有用,因为保守的基于规则的政策会导致不确定的等待。