Compared to manual driving, autonomous driving is more prone to the rapid
development and deterioration of pavement distress due to the concentration of
driving paths. Therefore, a reasonable and efficient maintenance strategy is
required. To address the challenges posed by the numerous constraints and
objectives in the maintenance strategy generation process, this paper proposes a
multi-objective optimization-based method for generating pavement maintenance
strategies. The approach leverages advanced pavement distress detection
technologies to establish an initial maintenance program, incorporating a range
of constraints and maintenance objectives, such as cost-efficiency, performance
longevity, and environmental impact. The method applies a genetic algorithm (GA)
to iteratively refine and optimize the maintenance strategy, ensuring that the
solutions align with both immediate and long-term performance goals for
autonomous vehicle operations. A case study utilizing real-world road data
demonstrates the effectiveness of the proposed optimization method. The results
indicate a significant improvement in the maintenance strategy's overall benefit
index, achieving a value of 4.37, with a 1.3-fold increase in benefit
performance ratio. Furthermore, when compared to conventional maintenance
approaches that apply a single repair method (e.g., micro-surfacing, hot
in-place recycling, or milling and overlay) across the entire route, the
optimized planning resulted in notable performance gains. Specifically, the
benefit performance ratios of the optimized plan increased by 6.92% for
micro-surfacing, 2.31% for hot in-place recycling, and 1.54% for milling and
overlay, demonstrating the advantages of tailored, multi-objective optimization.
This optimization method not only provides essential technical support for the
intelligent maintenance of autonomous driving routes but also offers valuable
insights for future multi-objective decision-making in transportation
infrastructure management. It lays the groundwork for more effective and
sustainable road maintenance strategies in the era of autonomous driving.