“ Investing in resilient , cybersecure , and agile cloud computing strategies that utilise powerful compute as well as igniting realtime analysis and decision making from edge devices is absolutely crucial ”
TECHNOLOGY
Policy Manager at the Institute of the Motor Industry . “ Non-mandatory ADAS features that are common include blind-spot monitoring , parking sensors , and forward-collision warning systems . All ADAS technologies are today improving safety and helping drivers stay alert and aware while on the road .”
As Robert Howard , ADAS Product Specialist at TomTom , explains , vehicles equipped with ADAS tech have the capacity to anonymously gather large amounts of data . This data , if used appropriately , can help to build more efficient and safer infrastructure for an automated driving future .
A bridge to driver acceptance of autonomy Driver assistance has been provided to many drivers through various means . But ADAS is becoming less of a luxury and more of a necessity , which can also be understood by looking at how Google manages traffic and provides real-time updates to drivers – allowing them to select the fastest , most economical routes based on traffic data and other factors .
“ Connectivity also enables ADAS features like remote monitoring and control . With the help of mobile apps or web portals , drivers can remotely monitor their vehicle ’ s performance , receive alerts about maintenance issues , and even control certain functions such as locking and unlocking the doors or starting the engine ,” Pells explains .
As Howard describes , connected cars create a dynamic environment for lowcost data sharing and aggregation , whereby massive amounts of anonymised data can be moved between the physical world and the cloud . “ Using AI , we can potentially provide real-time valuable insight into how roads are used , where traffic hotspots are , and how to better design infrastructure for automated driving safety .”
“ Investing in resilient , cybersecure , and agile cloud computing strategies that utilise powerful compute as well as igniting realtime analysis and decision making from edge devices is absolutely crucial ”
JORDAN MACPHERSON DIRECTOR OF PRODUCT OPERATIONS , PARK PLACE TECHNOLOGIES
There are many use cases for AI in vehicle engineering outside of self-driving cars , and one of the most potentially beneficial is monitoring driver awareness .
“ Several manufacturers are now using in-car computer vision-equipped cameras to monitor driver faces for microscopic indications of fatigue , which could provide early warning of tiredness that may lead
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