Drivers sometimes operate the accelerator pedal instead of the brake pedal due to driver error, which can potentially result in serious accidents. To address this, the Acceleration Control for Pedal Error (ACPE) system has been developed. This system detects such errors and controls vehicle acceleration to prevent these incidents. The United Nations is already considering regulations for this technology. This ACPE system is designed to operate at low speeds, from vehicle standstill to creep driving. However, if the system can detect errors based on the driver's operation of the accelerator pedal at various driving speeds, the system will be even more effective in terms of safety. The activation threshold of ACPE is designed to detect operational errors, and it is necessary to prevent the system from being activated during operational operations other than operational errors, i.e., false activation. This study focuses on the pedal operation characteristics of pedal stroke speed and pedal force speed, which used as the threshold for activation of ACPE. It examines the detection of pedal misapplication based on the operation characteristics of the accelerator and brake pedals during normal driving, with a particular emphasis on preventing false activations. We hypothesize that if there is a significant difference in the operations of the accelerator pedal and brake pedals while driving, it can be used as a threshold for judging misstep. In this study, we utilized a driving simulator to conduct driving experiments that simulated urban and highway environments. This allowed us to collect data on drivers' operations of the accelerator and brake pedals, including metrics such as pedal stroke speed and pedal force speed. The experimental scenario involved the driver following a car ahead that repeatedly accelerated and decelerated in both urban and highway areas. The results of the analysis of the pedal stroke speed and the pedal force speed showed that the brake pedal was operated faster than the accelerator pedal, with a significant difference confirmed. In addition, to prevent false activation and to improve the accuracy of detecting pedal misapplication, it is considered effective to incorporate factors such as relative velocity and ego vehicle speed into the activation thresholds, potentially set through machine learning or other methods.