Recently, we have been witnesses of accidents involving autonomous vehicles and their lack of sufficient information. One way to tackle this issue is to benefit from the perception of different view points, namely cooperative perception. We propose here a decentralized collaboration, i.e. peer-to-peer, in which the agents are active in their quest for full perception by asking for specific areas in their surroundings on which they would like to know more. Ultimately, we want to optimize a trade-off between the maximization of knowledge about moving objects and the minimization of the total volume of information received from others, to limit communication costs and message processing time. For this, we propose a way to learn a communication policy that reverses the usual communication paradigm by only requesting from other vehicles what is unknown to the ego-vehicle, instead of filtering on the sender side. We tested three different generative models to be taken as base for a Deep Reinforcement Learning (DRL) algorithm, and compared them to a broadcasting policy and a policy randomly selecting areas. In particular, we propose Locally Predictable VAE (LP-VAE), which appears to be producing better belief states for predictions than state-of-the-art models, both as a standalone model and in the context of DRL. Experiments were conducted in the driving simulator CARLA. Our best models reached on average a gain of 25% of the total complementary information, while only requesting about 5% of the ego-vehicle's perceptual field. This trade-off is adjustable through the interpretable hyperparameters of our reward function.

翻译：最近,我们目睹了涉及自治车辆的事故,以及这些车辆缺乏足够的信息。解决这一问题的一个方法就是从对不同观点,即合作观点的认识中受益。我们在此建议一种分散化的合作,即同行之间的合作,即代理人积极寻求全面认识,要求在其周围的特定领域建立他们希望更多了解的具体领域。归根结底,我们希望在最大限度地了解移动物体的知识与最大限度地减少从他人收到的信息总量之间实现最佳权衡,以限制通信成本和信息处理时间。为此,我们建议了一种学习改变通常通信模式的通信政策的方法,即只向其他车辆索取自我车辆所不知道的东西,而不是在发送方进行过滤。我们测试了三种不同的变异模型,作为深强化学习(DRL)算法的基础,并将它们与广播政策和随机选择区域加以比较。我们提议了可本地可预测的 VAE(LP-VAE),这似乎能让人们更清楚地了解我们正常的通信模式模式,而我们正以最佳的方式预测了25号A-BAR模型中的最佳模型。要求我们的平均模型,在25号A-BAR模型中实现。