The safety and robustness of autonomous vehicles continue to be some of the major concerns for developers today, with systems sometimes failing to detect obstacles and pedestrians, or malfunctioning due to false positives, corner cases, and other challenges. LiDAR technology has emerged as a potential solution to help fill this gap and makes self-driving safer.
Compared to driver state monitoring systems (DMS), or even occupant monitoring systems (OMS) for which we have experienced huge advances in terms of safety regulations and technology in recent years, the approach to in-cabin monitoring systems for public transport is relatively recent but closer than many might think.
Are near-infrared (NIR) cameras key to enabling in-vehicle sensing advances to pass the Euro NCAP adequation and safety assessment?
Anyverse™ is the new hyperspectral synthetic data platform that lands as the only solution to gather all the data developers require to build Euro NCAP compliant in-cabin monitoring systems cost and resource efficiently, as well as accelerating AV & ADAS development.
Real world details are infinite. When generating synthetic images simulating cameras, we need to be able to reproduce and capture as many details as possible from a computer generated 3D world as we would capture using real cameras in the real world. Don’t forget that, at the end of the day, the perception systems will use real cameras (and other sensors). Those details that we need to generate more faithfull images to feed our perception brain is what we call hyperspectral data.
Traditionally, if we can use that term to talk about technology as recent as in-cabin and driver monitoring systems, camera positioning has been bound above the dashboard and the center stack. But, are these camera placements optimal? Are these able to faithfully monitor the other occupants as well and not just the driver? Why stick to only these positions? Opening the door to simulation can help optimize the system without wasting budget, but let’s start from the beginning.
The self-driving market needs to reach the level of development where a driverless vehicle is 100% safe (and perceived as such by society) and reliable to operate like another manned vehicle in the vastness of the real world. In order to achieve this, one of the biggest challenges must be appropriately dealt with: the performance of autonomous driving in adverse weather conditions. How are you going to gather the data to achieve this?
SHARE Just like on a spooky Halloween night, anything sudden and unexpected could happen during a car trip… So better make sure your in-cabin monitoring system is well trained right? Driver monitoring, occupant monitoring, autonomous driving, and other autonomous deep learning-based visual systems… are critical use cases in which the safety of the occupants is …
Trick or treat, don’t let your in-cabin monitoring system AI be tricked! Read More »
In this insights series, we will focus on Anyverse Sensor Simulation and how to accelerate autonomous systems deployment with sensor-specific synthetic data.
In this insights series, we will focus on Anyverse Sensor Simulation and how to accelerate autonomous systems deployment with sensor-specific synthetic data.
