Driving style classification. The classification of drive styles is based on PCA and K-means clustering, with five key features extracted from the drive cycle data -total acceleration, total deceleration, braking frequency, total distance covered and average velocity. These features characterized driver behavior, with average velocity providing insights into the driver's operating speed range and serving as a valuable descriptor of driving style. We first apply PCA to reduce the dimensionality of the feature space while retaining their variance. This lower-dimensional representation is achieved by assigning weights to all features as shown in (Supplementary table ). The variance explained by each principal component is shown in the scree plot (Supplementary figure ). Following this, K-means clustering was employed to classify the samples in the principal component space into two distinct groups -assertive and defensive driving styles. The clustering process involved assigning each data point to the nearest cluster based on Euclidean distance, with the centroids of two groups iteratively updated. This process continued until the variance within the two clusters was minimized.