How drivers cope with automation

A new simulator to help car makers better understand how drivers will cope with and respond to the rising number of driver assistance (ADAS) and autonomous (AI) automotive technologies has been revealed by Ansible Motion.

The latest iteration of Ansible Motion’s Delta Driver-in-the-Loop (DIL) simulator provides a safe and repeatable laboratory environment to test and validate the myriad ADAS systems that are increasingly being fitted (or proposed) to new cars.

With the driver assistance systems market set to grow to $70bn by 2024, the issue of how real people might react to a car receiving more notifications – or even taking control – is one that car makers are investigating seriously.

‘Car makers are introducing more driver assistance technologies, but their level and method of intervention differs by car brand,’ said Kia Cammaerts, founder and director of Ansible Motion. ‘If a car does something unexpected, we are able to test what the driver and occupant reactions will be in our simulator laboratory, well in advance of cutting any metal. Our latest simulator enables car manufacturers to design better and safer vehicles and assess many proposed technologies early in the design cycle.’

To create immersive human simulation experiences Ansible Motion’s simulator lab in Hethel, Norfolk, has added a number of new features, such as new cabin environments that reflect OEM styling and human interaction features and new software connectivity that allows deeper environment and sensor simulation, coupled with Ansible Motion’s proprietary motion, vision, and audio environment that ‘tricks’ drivers and occupants into believing they are experiencing a real vehicle and its ADAS or autonomous technologies.

With the ability to create and explore a number of scenarios in a short amount of time, Ansible Motion’s simulator means engineers can conduct experimental variations that might consume a hundred years’ of testing time in the real world, within a few months. Examples include the validation of Autonomous Emergency Braking (AEB) systems that rely upon multiple sensor feeds and vehicle piloting logic algorithms to respond to various situations such as traffic and pedestrian intrusions. 

Other system validation examples include lane departure warnings and assistance, intelligent speed adaption and driver monitoring for drowsiness and distraction.