The safety of autonomous driving systems (ADSs) must be assessed through different scenarios before ADSs can be deployed. Scenario representation is a method to condense the infinite number of scenarios that can occur in real-world traffic into a finite set to facilitate testing. The scenarios are categorized into four levels: functional, abstract, logical, and concrete. A logical scenario is one in which the parameters representing the scenario are defined in the form of ranges. Research has been conducted on real-world traffic to derive reasonable ranges for logical scenario parameters. However, such studies incur elevated costs for scenario detection, lack consideration of hazardous situations, and suffer from driving style–related and regional biases in data. Therefore, this study introduces a simulation software–based method to overcome the cost and data bias problems while generating logical scenarios that account for both normal and hazardous situations. The proposed method involves analyzing the results for three of the scenarios specified in the ISO 34502 standard and qualitatively validating the findings against results from studies using real-world vehicle data.

This study introduces a real-time optimization framework for eco-driving of heavy-duty vehicles over long-distance routes. A longitudinal dynamic model incorporating powertrain performance and fuel consumption is formulated, and the eco-driving scenario is expressed as a quadratic programming (QP) problem. To improve computational efficiency, a novel variable-step spatial segmentation method is introduced, which ensures a balance between modeling accuracy and computational cost. Simulations involving mixed-terrain scenarios verify the effectiveness of the proposed approach. The results show that the QP-based method achieves fuel savings comparable to those offered by dynamic programming while significantly reducing computation time to sub-second levels; thus, the proposed strategy offers real-time applicability. These findings demonstrate the feasibility of global optimal velocity profile generation in practical eco-driving scenarios.