Big trends in autonomous vehicles

News28 Mar 2019In-Vehicle
Big trends in autonomous vehicles
The global automobile industry is worth a reported $3.5tn in annual revenues and is facing four huge disruptive threats: the connected car, electric vehicles, autonomous driving technology and the concept of transport-as-a-service.
It has been stated that there are some serious technology challenges ahead of autonomous vehicles, yet ‘vehicles continue to become increasingly aware of their operating environments, thanks to 3D cameras, radars, lasers, and sensor fusion’. And, thanks to thanks to ultra-fast computing enabling operational agility, advanced predictive analytics and machine learning, they are also now increasingly informed and intelligent.  

Reports suggest that within five years, the ‘remorseless’ advances in automobile technology will almost ‘totally reset the industry’s supply lines and value chains’. The 10 year view is said to have a more dramatic effect, yielding a motor industry largely based on ‘selling rides, deploying autonomous mobility and monetising miles’, an emergent industry that is thought to be as large as the current automotive industry.

A demand for sensors

The world of autonomous vehicles is said to demand huge volumes of sensors including vision, pressure, temperature sensors and more, to be able to feed live data sets to both in-vehicle computers as well as cloud-based data centres accessed remotely.

Edge computing

In live driving environments there is often little or no time to send data to the cloud and wait to receive details of what action to take.
Advances in Level 4 autonomy and beyond have developed to ensure that vehicles carry their own data centre on board that can ‘sense, infer and act in real time’, highlighting the importance of zero latency in real-time road and traffic conditions.

These on board computers are said to also play a ‘critical safety role in cordoning the vehicle off from external cyber-attack’.  Edge computing will naturally begin to take over more and more of the work that is currently being done in centralised cloud-based data centres.

Artificial Intelligence

It is reported that by 2030 ‘80% of the value of a vehicle will reside in its software and content’, with Artificial Intelligence consuming the software and precision targeting and then customising content. Neural net-based machine learning will become standard, whilst development of algorithm-specific AI chips that can merge processing and memory will be essential to progressing these intelligent vehicle auto brains.
'The ability for vehicles to learn and improve every mile they travel is front and centre of the evolving self-drive phenomenon'


Once widespread adoption of fully autonomous vehicles, able to handle journeys without human intervention and fully autonomous robot cars with no steering wheel or pedals, are deployed throughout the globe, it will be essential for 5G broadband wireless networks to be available.
5G mobile networks will require access to sufficient radio spectrum and be supported by universal base stations, in-device antennas, and denser yet cleaner-cut multiple inputs and multiple output (MIMO) systems in order to continue to serve the millions of connected ‘things’ in major cities.

Vehicles are said to need to be in constant communication with other vehicles, with smart driving environments and with intelligent cloud-based support. In the present climate, there may not be enough fast broadband multiple message frequencies for the demands that will be placed on the mobile internet industry by 2025.


The ever present threat of cyber-attacks are a constant amid the euphoria of the scope and prospects for self-drive. These attacks can come in many forms, from locking people out of their cars and demanding ransomware, malware that affects brakes and steering causing fatal accidents and sabotage of driving environments and GPS mapping.


Vehicle-to-everything (V2X) communication, when information is passed from a vehicle to another entity and vice versa, allowing the vehicle to communicate with the traffic system around them. Autonomous vehicles inherently require direct communication with road infrastructure to be able to report on updates like conditions, traffic, accidents and weather.

For this, there needs to be ‘sensor-rich driving environments’ within the smart cities and highways planned in order to progress this concept forward. Cities ultimately have to become smarter and highways need a greater level of embedded intelligence for the notion of self-drive to really take off.

Big data

'Self-drive vehicles will require access to huge datasets in order to educate them'. Some datasets may need to include ‘live time’ streaming, cameras and radar systems to enable critical navigation safety. And, as reported, in-vehicle and cloud data analytics and machine learning based on big data are crucial elements of self-drive.

As such, traditional car makers may struggle to stay relevant, as a key asset will be the vast amounts of sensor data collected from on-road vehicles, propelling self-drive in to a highly sought after and desired ‘must-have’ technology for the future.

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