Abstract
A Vehicular Ad-Hoc Network (VANET) helps vehicles send and receive environmental and traffic information, making it a crucial component towards fully autonomous roads. For VANETs to serve their purpose, there has to be sufficient coverage, even in less populated areas. Moreover, a lot of the safety information is time-sensitive; excessive delay in data transfer can increase the risk of fatal accidents. Unmanned Aerial Vehicles (UAVs) can be used as mobile base-stations to fill in gaps of coverage. The placement of these UAVs is crucial towards how well the system performs. We are particularly interested in the placement of mobile base-stations for a rural highway with sparse traffic, as it represents the worst-case scenario for vehicular communication. Instead of heuristic or linear programming methods for optimal placement, we use multi-agent reinforcement learning (MARL). The main benefit of MARL is that it allows the agents to learn model-free through experience. We propose a variation of the traditional Deep Independent Q-Learning. The modifications include an observation function augmented with information directly shared between neighbouring agents as well a shared policy scheme. We also implement a custom sparse highway simulator that is used for training and testing our algorithm. Our testing shows that the proposed MARL algorithm is able to learn the placement policies that produce the maximum rewards for different scenarios while adapting to the dynamic road densities along the service area. Our experiments show that our model is scalable, allowing the number of agents to increase without any modifications to the code. Finally, we show that our model can be generalized as the algorithm can be directly used and performs equally as well on an industry standard simulator. Future experiments can be performed to improve the realism and complexity of the highway models and adapting the algorithm to real-world scenarios.
Highlights
By 2030, experts predict that autonomous vehicles will displace most human driving [1]
Despite the works about multi-agent reinforcement learning (MARL) in vehicular networks, there has been no published results that we found directly using MARL to find optimal positions of drone-base-stations along a sparse highway
4) Generalizability: We show that our MARL algorithm can learn as well on an industry standard simulator as it does on our simplified custom simulator
Summary
By 2030, experts predict that autonomous vehicles will displace most human driving [1]. The purpose of VANETs is to help vehicles send and receive crucial environmental and traffic information as well as provide internet connection for passengers. This is important for enhancing safety features such as collision avoidance, road surface prediction, and improving efficiency of travel. Seliem et al [8] studied how delay can modelled under a sparse environment and proposed a Drone Active Service (DAS) to adjust the distance between the drone base-stations to provide sufficient coverage. Deep Reinforcement Learning uses neural networks to approximate these mappings This allows RL to be utilized on problems with more advanced state and action representations. MARL has been used for cellular networks to improve coverage and quality of service for users [16]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
