Ocular-Behavioral Metrics for Driver State Classification for Indian Driving Contexts: KSS-Based Evaluation, Conformance and Implementation Challenges ARAI

Format:

Book

Conference/Event

Author/Creator:

Verma, Harshal, author.

Contributor:

Kale, Jyoti Ganesh

Karle, Ujjwala

Conference Name:

Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

With the growing adoption of Advanced Driver Assistance Systems (ADAS) in the Indian automotive landscape, the need for effective Driver Monitoring Systems (DMS) has become increasingly critical. This paper presents the design, development, and validation of a Driver Distraction and Attention Warning System (DDAWS) tailored to Indian driving conditions. The proposed system integrates two key modules: Driver Attention Monitoring and Drowsiness Detection, using a high-resolution driver-facing camera to analyse head pose, facial landmarks, and behavioural cues. The drowsiness module incorporates metrics such as PERCLOS and Eye Aspect Ratio (EAR), evaluated against the Karolinska Sleepiness Scale (KSS). Recognizing the limitations of self-assessed scales like KSS in dynamic driving environments, the study compares algorithmgenerated KSS values with self-reported scores to assess model accuracy. Additionally, the framework aligns with automotive safety standards such as AIS184,EU 2021/1341, EU 2023/2590, and EURO-NCAP. A multi-level redundancy architecture is introduced to improve prediction robustness by fusing outputs from both attention and drowsiness subsystems. The result is a scalable, regulation-compliant, and reliable DDAWS framework, optimized for real-world deployment in Indian vehicles

Notes:

Vendor supplied data

Publisher Number:

2026-26-0668

Access Restriction:

Restricted for use by site license