Decision Mechanism of Vehicle Autonomous Lane Change Based on Rough Set Theory

As one of the most common driving behaviors, lane changing is a major factor affecting the safety of traffic vehicles and commuting efficiency, and it is also the key to achieving high-level autonomous driving. At present, rule-based decision-making models for lane change are mostly designed through expert experience and lack the corresponding data support. This paper combines rough set theory to extract lane change rules with high relevance and large sample size from natural driving data as decision-making conditions, and implements and verifies the model through Matlab and PreScan. The results show that the decision-making algorithm can fit the actual scene to make anthropomorphic decision-making instructions.