InCabin Brussels Agenda 2023

In this presentation, Euro NCAP will provide a status update and the next milestones on Euro NCAP’s Occupant Status Monitoring for 2026.

Hand position on wheel is one form of driver monitoring
Situation awareness not possible without camera. There is an argument for both hands on wheel and camera based (IIHS) to achieve ultimate safety outcome, but our view is that OEMs will not adopt both.

If one is to be dropped, which is ideal for the OEM to maintain and to meet safety standards?

Feature set potential with eye-gaze far outweighs value of hands on wheel sensors when it comes to Interior Sensing – HMI that leverages off what do you do with the detailed information our system can detect from driver using a camera-based monitoring system. Highlight what this enables from a sensitivitiy, notification and intervention (unintrusive) while maintaining a balance between driver acceptance and safety. Full integration with ADAS where sensitivities are managed to intervene appropriately using other systems in the car (eg. emergency braking, lane-keep assist, controlled stop) – getting to the pinnacle of interior-sensing

Road accidents are responsible for more than 1.25 million fatalities every year (WHO), with more than 90% of the cases caused by human errors. The implementation of Driver State Monitoring Systems (DMS) can significantly reduce driver errors caused by distraction and drowsiness, and the EU General Safety Regulation (GSR) Phase I mandates that all new passenger and commercial vehicles in the EU must have Driver Drowsiness and Attention Warning (DDAW) functionality from 2024 and Phase II requires Advanced Driver Distraction Warning (ADDW) as a mandatory feature from 2026. Although there are growing number of DMS systems installed in both commercial and passenger vehicles, the type-approval for those vehicles with regards to the regulation (EU) 2021/1341 (DDAW) (hereafter “the Regulation”) is still new. Recently TÜV SÜD worked with ArcSoft on a validation test for the ArcSoft Tahoe In-cabin Monitoring System (hereafter “Tahoe” or “DDAW system”). In this report we will present how the validation test has been conducted and some interesting results and observations. Founded in 1994, ArcSoft is a leading algorithm and software solutions provider in the computer vision industry, with applications in both automotive and other fields. ArcSoft Tahoe is a camera-based driver monitoring system. It is composed of an automotive-grade highperformance AI processor, a high-definition camera, and full-featured DMS application software. It is a standalone DMS solution that can be installed at various locations in the cockpit. Tahoe has the functionality of both DDAW and driver distraction warning as well, it supports the standard CAN communication interface, and can output the DMS results in real time through the CAN connection. TÜV SÜD Czech was responsible for all activities of the validation testing. The Regulation provides general description of the validation testing in Annex I, part 2. However, the test methodology had to be defined in detail and applied. It was based on the essential principles:
• To meet the requirements of the Regulation
• Safety strategy to minimize risk of accidents
• Simple installation of the test tool chain with minimal modification of the test vehicle
• Modular setup of the test tool chain for uniform application regardless the applied technology of the system under test and the test vehicle
• Effective execution of the test within dedicated time slot
• Traceable data recorded during the test for further analysis
• Possibility to evaluate the test results immediately. The tests were carried out at a proving ground as a part of safety strategy, using human participants in accordance with the Regulation, where the self-assessment rating provided by test driver meets the criteria in accordance with the Regulation, Annex I, part 2. The tests were carried out independently to environmental conditions, since the DDAW system is less affected by light conditions. Drowsiness was measured using the KSS in accordance with Annex 1 part 2, chapter 5.1. The test results were evaluated in accordance with chapter 7. The acceptance criteria were applied in accordance with the points 8.1 a) and b) with correction stated at point 8.1 c) as some test runs were longer than 15 minutes. The validation testing confirmed that the camera based DDAW system is able to monitor driver drowsiness as required in accordance with the Regulation, Annex I, part 2, point 1.1. The validation testing of the DDAW system met the requirements set out in accordance with the Regulation, Annex I, part 2, points 2 to 8.

The various uses cases for emotion recognition in a vehicle
The differences between various emotion modalities the key technical challenges of developing and deploying visual emotion recognition in a vehicle. The near-term in vehicle applications of visual emotion recognition. Demo/video examples

Abstract: In-cabin monitoring systems use cameras for fundamental detection of the driver and occupant activity. Within the camera, the image sensor is a key component for converting the incoming light into a signal to be used by downstream machine vision algorithms. In-cabin cameras use image sensors designed to be optimized to a near infrared (NIR) signal, as this segment of the electromagnetic spectrum can illuminate the driver and occupant without interference of the primary driving task inside the vehicle. Quantifying the camera’s NIR performance, unlike traditional cameras that use tools designed for the visible spectrum, requires image quality tools optimized for NIR. This talk will describe such tools that can be used to measure image quality factors including sharpness, noise, distortion, flare, and tonal properties such as dynamic range. We will also be describing a novel approach to measure information capacity and related KPIs (key performance indicators). These measurements can then be used to optimize the camera systems to increase performance of the driver and occupant monitoring systems (DMS and OMS).