DUIncoder: Learning to Detect Driving Under the Influence Behaviors from Various Normal Driving Data

Abstract

Driving Under the Influence (DUI) has emerged as a significant threat to public safety in recent years. Despite substantial efforts to effectively detect DUI, the inherent risks associated with acquiring DUI-related data pose challenges in meeting the data requirements for training. To address this issue, we propose DUIncoder, which is an unsupervised framework designed to learn exclusively from normal driving data across diverse scenarios to detect DUI behaviors and provide explanatory insights. DUIncoder aims to address the challenge of collecting DUI data by leveraging diverse normal driving data, which can be readily and continuously obtained from daily driving. Experiments on simulator data show that DUIncoder achieves detection performance superior to that of supervised learning methods which require additional DUI data. Moreover, its generalization capabilities and adaptability to incremental data demonstrate its potential for enhanced real-world applicability.

Keywords: driving behavior; driving under influence; increment learning; unsupervised learning.

Figure A1

Training and validation loss curves for driver models in DUIncoders: (a) training loss in DUIncoderR; (b) validation loss in DUIncoderR; (c) training loss in DUIncoderG; (d) validation loss in DUIncoderG.

Figure 1

Illustration of two approaches for detecting DUI driving behaviors: (a) detect DUI driving behaviors using binary classification; (b) detect DUI driving behaviors using novelty detection.

Figure 2

Illustration of the overall structure of DUIncoder.

Figure 3

Network structure of the driver model in DUIncoder.

Figure 4

Illustration of the implement process of DUIncoder: (a) training process of the driver model; (b) validating process of the driver model; (c) fitting process of the novelty detector; (d) inferring process of DUIncoder.

Figure 5

The driving simulator for collecting both normal and drunk driving behaviors.

Figure 6

Examples of scenes in the three different routes: ((top): route “near accident urban”; (middle): route “accident”; (bottom): route “highway”). The green point in the trajectory maps indicates the vehicle’s current location.

Figure 7

Flow diagram of the data collection process.

Figure 8

Example of driving behaviors during a journey: (a) vehicle velocity (km/h); (b) steering angle ([−1, 1]); (c) throttle position ([0, 1]); (d) brake position ([0, 1]); (e) lane center deviation (m); (f) yaw rotation speed (rad/s).

Figure 9

Box-plot comparison with baseline methods in terms of detecting DUI driving behavior in normal and drunk records across all three different routes.

Figure 10

Trajectory comparison with baseline methods in terms of detecting DUI driving behavior in normal and drunk records across all three different routes. The red points in the trajectory denote frames predicted as drunk driving.

Figure 11

Comparison of actual and predicted driving behaviors in anomalous segments detected by DUIncoderR in “accident” route: (a) the red section within the dashed box indicates the location of the selected detected anomalous segment within the entire trajectory; (b) a comparison of actual and predicted driving behaviors within this segment, where orange represents the predicted driving behavior, and blue represents the actual driving behavior: (i) vehicle velocity (km/h); (ii) steering angle ([−1, 1]); (iii) throttle position ([0, 1]); (iv) brake position ([0, 1]); (v) lane center deviation (m); (vi) yaw rotation speed (rad/s).

Figure 12

Box-plot comparison with baseline methods in terms of detecting DUI driving behavior in normal and drunk records of three different routes, respectively: (a) route “accident”; (b) route “urban”; (c) route “highway”.

Figure 13

Box-plot comparison of DUI behavior detection by DUIncoders trained on different scenarios in normal and drunk data for three routes: (a) comparison between DUIncoderGs on all three routes; (b) comparison between DUIncoderGs on route “accident”; (c) comparison between DUIncoderGs on route “urban”; (d) comparison between DUIncoderGs on route “highway”; (e) comparison between DUIncoderRs on all three routes; (f) comparison between DUIncoderRs on route “accident”; (g) comparison between DUIncoderRs on route “urban”; (h) comparison between DUIncoderRs on route “highway”.

Figure 14

Box-plot comparison of DUI behavior detection by DUIncoders using different size of sliding window for route “accident” and route “urban”: (a) different window size setting for DUIncoderG; (b) different window size setting for DUIncoderR.

Figure 15

Box-plot comparison of DUI behavior detection by DUIncoders using different hyperparameter settings of novelty detector for route “accident” and route “urban”: (a) different settings for DUIncoderG; (b) different settings for DUIncoderR.

Figure 16

Box-plot comparison of DUI behavior detection by DUIncoders using different proportion of training data for route “accident” and route “urban”: (a) different settings for DUIncoderG; (b) different settings for DUIncoderR.