OpenConvoy: Universal Platform for Real-World Testing of Cooperative Driving Systems

Cooperative driving, enabled by communication between automated vehicle systems, promises significant benefits to fuel efficiency, road capacity, and safety over single-vehicle driver assistance systems such as adaptive cruise control (ACC). However, the responsible development and implementation of these algorithms poses substantial challenges due to the need for extensive real-world testing. We address this issue and introduce OpenConvoy, an open and extensible framework designed for the implementation and assessment of cooperative driving policies on physical connected and autonomous vehicles (CAVs). We demonstrate the capabilities of OpenConvoy through a series of experiments on a convoy of multi-scale vehicles controlled by Platooning to show the stability of our system across vehicle configurations and its ability to effectively measure convoy cohesion across driving scenarios including varying degrees of communication loss.

Knowledge Graph Reasoning with Self-supervised Reinforcement Learning

Reinforcement learning (RL) is an effective method of finding reasoning pathways in incomplete knowledge graphs (KGs). To overcome the challenges of a large action space, a self-supervised pre-training method is proposed to warm up the policy network before the RL training stage. To alleviate the distributional mismatch issue in general self-supervised RL (SSRL), in our supervised learning (SL) stage, the agent selects actions based on the policy network and learns from generated labels; this self-generation of labels is the intuition behind the name self-supervised. With this training framework, the information density of our SL objective is increased and the agent is prevented from getting stuck with the early rewarded paths. Our self-supervised RL (SSRL) method improves the performance of RL by pairing it with the wide coverage achieved by SL during pretraining, since the breadth of the SL objective makes it infeasible to train an agent with that alone. We show that our SSRL model meets or exceeds current state-of-the-art results on all Hits@k and mean reciprocal rank (MRR) metrics on four large benchmark KG datasets. This SSRL method can be used as a plug-in for any RL architecture for a KGR task. We adopt two RL architectures, i.e., MINERVA and MultiHopKG as our baseline RL models and experimentally show that our SSRL model consistently outperforms both baselines on all of these four KG reasoning tasks. Full code for the paper available at https://github.com/owenonline/Knowledge-Graph-Reasoning-with-Self-supervised-Reinforcement-Learning.