Collision-free parking recommendation based on multi-agent reinforcement learning in vehicular crowdsensing

The recent proliferation of Fifth-Generation (5G) networks and Sixth-Generation (6G) networks has given rise to Vehicular Crowd Sensing (VCS) systems which solve parking collisions by effectively incentivizing vehicle participation. However, instead of being an isolated module, the incentive mechanism usually interacts with other modules. Based on this, we capture this synergy and propose a Collision-free Parking Recommendation (CPR), a novel VCS system framework that integrates an incentive mechanism, a non-cooperative VCS game, and a multi-agent reinforcement learning algorithm, to derive an optimal parking strategy in real time. Specifically, we utilize an LSTM method to predict parking areas roughly for recommendations accurately. Its incentive mechanism is designed to motivate vehicle participation by considering dynamically priced parking tasks and social network effects. In order to cope with stochastic parking collisions, its non-cooperative VCS game further analyzes the uncertain interactions between vehicles in parking decision-making. Then its multi-agent reinforcement learning algorithm models the VCS campaign as a multi-agent Markov decision process that not only derives the optimal collision-free parking strategy for each vehicle independently, but also proves that the optimal parking strategy for each vehicle is Pareto-optimal. Finally, numerical results demonstrate that CPR can accomplish parking tasks at a 99.7% accuracy compared with other baselines, efficiently recommending parking spaces.