A machine learning study for predicting driver goals in contingencies with leading and lagging features during goal determination

Hsueh Yi Lai*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Many studies have focused on decision support systems that enhance both the efficiency and safety of driving. They have also explored the potential of real-time psychological data and machine learning in predicting drivers’ cognitive state, such as their fatigue levels, drowsiness, or workload. However, few studies have investigated prediction of driving goals as a cognitive outcome. Early prediction plays an essential role in providing active decision support during driving events under time pressure conditions. In this study, machine learning algorithms and features associated with different phases of decision-making were used to predict two common driving goals: defensive driving in emerging scenarios and urgent reactions in nonroutine scenarios. The effects of perception-, reflex-, control-, and kinetic-related features and how they contribute to prediction in the context of decision-making were analyzed. A total of 49 individuals were recruited to complete simulated driving tasks, with 237 events of defensive driving and 271 events of urgent reactions identified. The results revealed premium recall with a naïve Bayes classifier, indicating the onset of decision-making, with extreme gradient boosting and random forests exhibiting superior precision in predicting defensive driving and urgent reactions, respectively. Additionally, the cutoff of the initial 0.4 seconds of the events was identified. Before the cutoff, the leading features were reflex- and control-related features, which were the drivers’ immediate reactions before scenario evaluation and goal determination. These leading features contributed to superior prediction results for the two types of driving goals, indicating the likelihood of early detection. After the cutoff, model performance decreased, and lagging features came into play. These lagging features comprised perception- and kinetic-related features, reflecting observation of cues and outcomes of inputs delivered to vehicles. In the first 2 seconds, predictive models recovered and stabilized.
Original languageAmerican English
Article number121864
JournalExpert Systems with Applications
Volume238
DOIs
StateAccepted/In press - Sep 2023

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