The Longitudinal Driving Behavior of a Vehicle Assisted with Lv2 Driving Automation: An Empirical Study

Table 8

Description of the genetic algorithm settings according to MATLAB.

Algorithm settings	Value/method	Short description
Population size	500	Specifies how many individuals there are in each generation.
Maximum generations	800	Defines the maximum number of iterations for the GA to perform.
Stall generations	150	Calculates the weighted relative change in the objective function value over stall generations.
Fitness scaling	Rank	Function that scales and sorts individuals based on the values of the objective function.
Parent selection	Stochastic	Function that selects parents for the next generation based on their scaled values.
Children reproduction	Elite and crossover	Selects elite (0.05) and crossover (0.8) children for the next generation. Rest of the children are produced by mutation operations.
Crossover	Scatter	Indicates how two individuals form a crossover child for the next generation.
Mutation	Gaussian (mean 0)	Indicates how two individuals form a mutation child for the next generation.
Tolerance function		Exit criteria.