Machine Learning for Autonomous Vehicle Traffic Congestion Prediction and Mitigation

Abstract

Estimates reveal that the worldwide economic loss caused by traffic congestion is at least $100 billion in 1980 and has very high growth annually until now. Spreading congestion prevention strategies has become a potential perspective for the widespread acceptance of AVs. In the automotive memory system, there are two ways, i.e., complete memory and gradual memory. Since the complete memory system cannot cope with generalization, the overwhelming majority of architectures now use a memory map with partial memory architecture. These techniques can significantly improve the AV’s ability to understand traffic and learn driving strategies. Additionally, the traffic congestion problem is one of the most significant social challenges that the AV traffic system can help solve. Merged with AVs, a clear mobility system will make it possible to predict the state of the traffic congestion in advance and to plan for both travelers and vehicle controllers to make a detour to avoid congestion, which will seriously alleviate the traffic jam situation. In addition to affecting processing forecasting, the ability to predict traffic conditions can significantly reduce CO2 emissions.