Mercedes On Lease have a limited number of A Class A180 d Sport Hatchback Auto’s in stock which we can offer from only £129 + vat per month!

Mercedes A Class A180 d Sport 5dr Hatchback
24 Month Business Contract Hire, 10,000 miles per annum, Non-maintained
Initial rental £3,250.00 + vat
23x monthly rentals £139.95 + vat

Mercedes A Class A180 d Sport 5dr Hatchback
24 Month Personal Contract Hire, 10,000 miles per annum, Non-maintained
Initial rental £3,900.00 Inc vat
23x monthly rentals £169.99 Inc vat

To apply for this offer please click on the following link and complete the finance application relevant to your circumstances.

Mercedes-Benz Finance Application

On submission of your application we aim to have a decision for you within 24hrs. Please be aware that by submitting a finance application by no way commits you to anything at this stage, however a credit search will be carried out and will appear on your credit file.

Mercedes On Lease are required by the Financial Conduct Authority (FCA) to provide you with an Initial Disclosure Document to enable you to decide whether or not the products and services we offer are suitable for your needs. Please Click Here to view our Initial Disclosure Document.

Should you have any queries regarding the above offer please do not hesitate to visit our FAQ’s page or contact us on 01202 087822

Business & Personal Contract Hire is subject to credit status and acceptance. This is a hire product so you will not own the vehicle. A documentation fee of £245 plus VAT applies to this offer. Offer pricing is subject to change at any time. Figures may vary if a factory order is required. Images for illustration purposes only. At the end of your agreement the vehicle must be in a condition relevant to the vehicles age, mileage with fair wear and tear excepted. If the vehicle exceeds the contract mileage an excess mileage charge will apply. Additional charges may apply if you terminate the contract early.

According to some UK media outlets, the UK is in ‘pole position’ and already leading the world to a driverless future. Multiple headlines over recent months have promised autonomous vehicles will be on our roads by the end of this year, or maybe by 2021; it depends on which papers and websites you follow.

Driverless car investment and testing

Certainly, the UK government has been investing in the technology needed for autonomous vehicles. A grant of £8.6 million was given by Innovate UK to a consortium led by Oxbotica – an autonomous car developer – to run trials of their vehicle in the UK late last year. So the political will is certainly there.

Driverless car testing is also taking place around the UK, from London, Hounslow, to the streets of Oxford and Milton Keynes, driverless cars are being put to the test both with and without humans behind the wheel.

Driverless car development

However, it’s not just the political will that’s needed to make autonomous vehicles a reality on our roads.  The development of reliable, robust and affordable technology to enable autonomous vehicles to work safely is also needed, along with the necessary infrastructure, and consumer acceptance.

Several automotive and technology companies are racing to get their driverless cars on the roads before the competition and there’s big money at stake for the winners. The autonomous vehicle market is expected to grow from $54.23 billion this year to $556.67 billion in 2026, according to Allied Market Research.

Mercedes-Benz carmaker Daimler has teamed up with BMW and recently announced a goal to unveil driverless robot taxis early next decade. They’ve secured licences to test their self-driving cars on public roads in Germany, China and the USA too; making them the first foreign company to gain such permissions in China.

Volvo has already developed level 2+ driverless vehicles using Nvidia’s Drive AGX Xavier computer to power their system. These are expected to go into mass production early next year. Nvidia, Continental, Oxbotica, Addison Lee and a number of other companies are also working on their own driverless car technology.

An independent review of driverless cars in the UK

While our government and media might tout the UK as leading the pack in terms of a driverless future; one study, looking into the four key areas needed to make this possible disagreed.

After ranking countries for government support and oversight, excellent motorway infrastructure, large-scale innovation and general consumer acceptance, the UK wasn’t anywhere near the top. In fact, out of the 20 countries reviewed, the UK came a middling 10^th with The Netherlands, Singapore and Japan winning the top three spots.

Given the various elements needed to put the UK in pole position to lead the world to a driverless future, it would seem there’s a lot more work that needs doing.

With all the talk in the news about the UK leading the driverless car race, have you ever wondered what technology is used to enable an autonomous vehicle to see? How will these cars of the future navigate the roads, obey the road rules and differentiate between a pheasant and a child running across their path?

Dozens of automotive and tech companies are working to bring driverless cars to our roads. From luxury car manufacturers like Mercedes-Benz to the Google car, the race is clearly on and has been running for at least the last five years. There’s a lot to get to grips with – driving control is just a small (and possibly the easiest) part of the puzzle. Enabling a driverless car to see and make sense of what it sees is one of the problems that have engineers and programmers scratching their heads. Below we take a look at the three main types of vision technology for driverless cars.

Cameras

Starting with the technology that most of us are familiar with, cameras are generally placed on the roof, sides and bumpers of the vehicle. A dozen or more 3D cameras can be used, and sometimes placed in stereo, to see the surroundings – traffic lights, roads signs and the like. They can see in enough detail to recognise a child running onto the road, but they can only see in daylight or what is lit up by your headlights. So they’re only as good as you or me in poor weather conditions.

Radar

Used in cars for around two decades now for driver assistance packages, radar is reliable and unhindered by bad weather. It can detect obstacles from 160 metres away, or more, along with the speed and direction they are travelling. It can’t figure out what objects actually are though, so not so great for assisting with map building or object recognition.

LiDAR

Light Detection and Ranging (LiDAR) uses pulsed lasers to measure distances from it to objects around it. Shooting up to 1,000,000 pulses per second, the LiDAR system calculates how long each pulse takes to return, thus creating a map of static and mobile objects around it. It works in day or night conditions and some systems an also detect speed and direction of moving objects. Unfortunately, it doesn’t work nearly as well in rain or fog as the light can be bounced back from the water particles in the air and secondly, most LiDAR sensor systems are prohibitively expensive although various tech companies are working to build reliable and less expensive systems.

Making Sense of the Data

Most car manufacturers venturing into the driverless car arena are using a combination of all the sensors described above, or at least two. Elon Musk of Tesla has famously poo-hoed LiDar, but for now, he’s pretty much on his own.

Regardless of which sensors are used and in what combination, what’s done with the information collected is when things really get interesting. Making sense of the continuous flow of visual input requires a process that some refer to as sensor fusion and others might call Simultaneous Localization and Mapping (SLAM). Regardless of the name or acronym used, the driverless car needs to process the information from the numerous sensors to understand what surrounds it, where it is in regards to those surroundings, the importance of various objects detected (e.g.: small child Vs pheasant) and the best path to get from A to B. Machine learning, Artificial Intelligence and computer neural pathways all come into their own when trying to solve that problem for driverless cars.