DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data (2012.15441v2)

Abstract: Automated vehicles promise a future where drivers can engage in non-driving tasks without hands on the steering wheels for a prolonged period. Nevertheless, automated vehicles may still need to occasionally hand the control back to drivers due to technology limitations and legal requirements. While some systems determine the need for driver takeover using driver context and road condition to initiate a takeover request, studies show that the driver may not react to it. We present DeepTake, a novel deep neural network-based framework that predicts multiple aspects of takeover behavior to ensure that the driver is able to safely take over the control when engaged in non-driving tasks. Using features from vehicle data, driver biometrics, and subjective measurements, DeepTake predicts the driver's intention, time, and quality of takeover. We evaluate DeepTake performance using multiple evaluation metrics. Results show that DeepTake reliably predicts the takeover intention, time, and quality, with an accuracy of 96%, 93%, and 83%, respectively. Results also indicate that DeepTake outperforms previous state-of-the-art methods on predicting driver takeover time and quality. Our findings have implications for the algorithm development of driver monitoring and state detection.

• The paper introduces DeepTake, a deep learning framework that accurately predicts driver takeover intention, timing, and quality using multimodal data.
• The methodology integrates driver biometrics, vehicle telemetry, and self-assessments within a 10-second pre-event window to capture takeover behavior.
• Results show DeepTake significantly outperforms traditional models, enhancing safety and decision-making in automated vehicle control transitions.

Overview of DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data

The paper introduces "DeepTake," a deep neural network (DNN)-based framework designed to predict driver takeover behavior in automated vehicles (AVs) using multimodal data sources. DeepTake seeks to address the challenges faced by AV systems in ensuring effective and safe handovers to human drivers, particularly when the vehicle encounters conditions that exceed its autonomous capabilities. The paper is grounded in the need for AVs to anticipate various facets of a driver's takeover capability, focusing on intention, time, and quality of the takeover attempt.

DeepTake integrates data from multiple sources, including vehicle telemetry, driver biometrics such as eye movements and heart rate variability (HRV), and subjective self-assessments. By combining these diverse data streams, the framework aims to enhance the accuracy of predictions compared to previous models which often focused on singular aspects of takeover behavior or relied on narrower datasets. The paper's authors assert that DeepTake's predictive accuracy surpasses existing methodologies, citing classification accuracies of 96%, 93%, and 83% for takeover intention, time, and quality, respectively.

Methodology

The architecture of DeepTake hinges on the employment of DNN models to analyze and synthesize information from its varied data inputs. These include:

• Driver Biometrics: Captured using wearable sensors, providing real-time data on eye movement and physiological stress indicators.
• Pre-Driving Surveys: Collecting subjective data on drivers' perceived workload and stress levels, which could affect their response to takeover prompts.
• Non-Driving Related Tasks (NDRTs): Assessing the impact of secondary tasks, such as cellphone use or reading, on a driver's readiness to retake control.
• Vehicle Data: Monitoring variables like lane position, speed, and acceleration to contextualize the environment in which takeover prompts occur.

DeepTake employs a 10-second pre-event window for data processing, providing a temporal snapshot leading up to a takeover request (TOR). The model is trained with a dataset enriched through driver simulation studies, with a focus on real-world plausibility in AV scenarios.

Results and Discussion

The framework was benchmarked against traditional machine learning models like Logistic Regression and Random Forest, where DeepTake demonstrated superior performance across all predictive metrics. It significantly outshined these models in both binary and multi-class classification tasks concerning driver takeover intention and performance.

The paper emphasizes that DeepTake's multi-faceted approach to data collection and analysis enables a deeper understanding of the nuanced factors influencing driver behavior during AV operation. This nuanced understanding is essential for developing intelligent systems that manage the transition of control dynamically, thereby reducing transition times and improving driver safety in unexpected events.

Implications and Future Work

DeepTake's integrated approach exemplifies the potential for AI to enhance driver-vehicle interaction in semi-autonomous environments. By accurately forecasting a driver's capacity to resume control, the system could better inform AV design and facilitate safer transitions that accommodate individual driver behavior.

While promising, the paper acknowledges the constraints posed by the simulated environments used in testing. Future research may focus on validating these models under more variable real-world conditions, potentially incorporating additional data facets like road conditions and in-vehicle communication patterns. Moreover, there's potential for refining the model to predict takeover time numerically rather than categorically, providing continuous predictive metrics for more tailored interventions.

In conclusion, DeepTake represents a meaningful advancement in AV interface design, offering improvements in the safety and efficiency of human-automation cooperation. Its successful implementation may lead to more adaptive and personalized AV systems, contributing to broader acceptance and reliability of automated transportation technologies.