Local mapping is valuable for many real-time applications of intelligent vehicle systems. Multi-vehicle cooperative local mapping can bring considerable benefits for vehicles operating in some challenging scenarios. In this paper, we introduce a method of occupancy grid map merging, dedicated to multi-vehicle cooperative local mapping purpose in outdoor environment. In a general map merging framework, we propose an objective function based on occupancy likelihood, and provide some concrete procedures designed in the spirit of genetic algorithm to optimize the defined objective function. Based on the introduced method, we further describe a strategy of indirect vehicle-to-vehicle relative pose estimation, which can serve as a general solution for multi-vehicle perception association. We present a variety of experiments that validate the effectiveness of the proposed occupancy grid map merging method. We also demonstrate several useful application examples of the indirect vehicle-to-vehicle relative pose estimation strategy.

Vehicle-to-Everything (V2X) communication enhances the capability of autonomous driving through better safety, efficiency, and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this paper, we describe a roadside perception unit (RSPU) that combines sensors and roadside units (RSUs) for infrastructure-based cooperative perception. We propose a software called AutoC2X that we designed to realize cooperative perception for RSPUs and vehicles. We also propose the concept of networked RSPUs, which is the inter-connection of RSPUs along a road over a wired network, and helps realize broader cooperative perception. We evaluated the RSPU system and the networked RSPUs through a field test, numerical analysis, and simulation experiments. Field evaluation showed that, even in the worst case, our RSPU system can deliver messages to an autonomous vehicle within 100 ms. The simulation result shows that the proposed priority algorithm achieves a wide perception range with a high delivery ratio and low latency, especially under heavy road traffic conditions.

The realization of vehicle-to-everything (V2X) communication enhances the capabilities of autonomous vehicles in terms of safety efficiency and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this regard, open-source software plays a significant role in prototyping, validation, and deployment. Specifically, in the developer community, Autoware is a popular open-source software for self-driving vehicles, and OpenC2X is an open-source experimental and prototyping platform for cooperative ITS. This paper reports on a system named AutoC2X to enable cooperative perception by using OpenC2X for Autowarebased autonomous vehicles. The developed system is evaluated by conducting field experiments involving real hardware. The results demonstrate that AutoC2X can deliver the cooperative perception message within 100 ms in the worst case.

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