Tanzanian transport regulator SUMATRA is looking to use a smart tracking system to reduce traffic accidents by monitoring vehicle speeds of public service vehicles, writes RUSSELL SOUTHWOOD.
Muchangwe Ferrao looks at how a vehicle monitoring system from a Tanzanian start-up called CUMii can tackle this task.
Tanzania loses thousands of lives each year through road traffic accidents. In 2015, the Bureau of Statistics recorded about 10,000 injuries, and 3500 deaths as a result of carnage on the roads. The government now seeks to implement intelligent systems such as ConnectCar to make the country’s roads safer.
With an estimated population of 4.4 million, Dar es Salaam operates a mixed transport system that includes boda-bodas (motor-bikes), bajaj (three-wheel motorized cart), and dala dalas (commuter bus), all servicing main metropolitan areas. Being a port town, Dar es Salaam also serves as transit/gateway for large amounts of local and international traffic, which makes its road network not only busy, but also highly treacherous.
The volume of traffic presents a challenge for the governing body – Surface and Marine Transport Regulatory Authority (SUMATRA) – in managing safety effectively. In the past, SUMATRA has used traditional electrical speed monitors but these have been ineffective as drivers easily tamper with them, thus making data recovered unreliable.
In a tender recently issued by the regulator, the primary mandate set by the government is to reduce the number of traffic accidents by implementing a smart tracking system. The system will not only monitor and track all passenger carrying motor vehicles, but essentially give control to the authority, as well as fleet owners to enhance safety, reduce idling time, manage fuel costs, as well as insurance premiums by having real time business changing data at their finger tips.
In Tanzania, Vodacom has partnered with CUMii, a leading African company in disruptive technology, to introduce ConnectedCar.
ConnectedCar is a Machine-to-Machine (M2M) tracking service that monitors driver behavior using a remote management platform. Once installed, ConnectedCar can converge with different devices including smart phones, cameras, gas readers, and many more via the Internet. It is easily configured to meet user requirements and using a mobile app or web interface displays information such as:
– Driving habits – e.g. hard braking
– Trip reports & customised reports
– Geo fencing
– Real time tracking
– Battery Tampering Alerts
– Multiple Driver tags & Panic button
– Fuel Management
– Violations notifications via SMS, email
– Route Planning and Management
This information offers both control over safety and security, savings on insurance premiums and much more. Effectively it is an all round cost management product.
According to Chief Officer – Business Enterprise, Gregory Verbond: “Connected devices can be used to improve public safety, conserve resources, boost productivity and support the government effectively. Tanzania stands to benefit by implementing M2M solutions that bring hardware, software and data analytics together in a single solution which is what this partnership brings to the table.”
The unique features of this technology can aid in other areas where governments and enterprise seek to curb fraud through abuse of assets. In a recent media statement, Commissioner of Policy and Procurement, Frederic Mwakibinga emphasised the need for more stringent monitoring tools to eliminate the mis-use of government vehicles in Tanzania. ConnectedCar can help to achieve this too.
Research has shown that markets that have implemented this technology effectively have resulted in profits of up-to 12% experienced through cost saving and management. This is a substantial amount to a country like Tanzania where road accidents cost the government approximately Tshs. 20 billion annually.
In cities such as Dar es Salaam where road accidents due to negligence and reckless driving are part of daily life, ConnectedCar, can make a real difference. In its simplest form, this service is friendly enough to be used by anyone concerned about the safety of the of loved ones, bajaj owners and families alike.
Local engineers known as Technites, who are trained and accredited by CUMii, do the installations. This model aims to ensure quality and excellence, as well as enhance local skills whilst creating new jobs in the local market.
* Russell Southwood is editor of Smart Monkey TV. To subscribe to its web TV channel, visit http://www.youtube.com/user/SmartMonkeyTV/videos
Project Bloodhound saved
The British project to break the world landspeed record at a site in the Northern Cape has been saved by a new backer, after it went into bankruptcy proceedings in October.
Two weeks ago, and two months after entering voluntary administration, the Bloodhound Programme Limited announced it was shutting down. This week it announced that its assets, including the Bloodhound Supersonic Car (SSC), had been acquired by an enthusiastic – and wealthy – supporter.
“We are absolutely delighted that on Monday 17th December, the business and assets were bought, allowing the Project to continue,” the team said in a statement.
“The acquisition was made by Yorkshire-based entrepreneur Ian Warhurst. Ian is a mechanical engineer by training, with a strong background in managing a highly successful business in the automotive engineering sector, so he will bring a lot of expertise to the Project.”
Warhurst and his family, says the team, have been enthusiastic Bloodhound supporters for many years, and this inspired his new involvement with the Project.
“I am delighted to have been able to safeguard the business and assets preventing the project breakup,” he said. “I know how important it is to inspire young people about science, technology, engineering and maths, and I want to ensure Bloodhound can continue doing that into the future.
“It’s clear how much this unique British project means to people and I have been overwhelmed by the messages of thanks I have received in the last few days.”
The record attempt was due to be made late next year at Hakskeen Pan in the Kalahari Desert, where retired pilot Andy Green planned to beat the 1228km/h land-speed record he set in the United States in 1997. The target is for Bloodhound to become the first car to reach 1000mph (1610km/h). A track 19km long and 500 metres wide has been prepared, with members of the local community hired to clear 16 000 tons of rock and stone to smooth the surface.
The team said in its announcement this week: “Although it has been a frustrating few months for Bloodhound, we are thrilled that Ian has saved Bloodhound SSC from closure for the country and the many supporters around the world who have been inspired by the Project. We now have a lot of planning to do for 2019 and beyond.”
Motor Racing meets Machine Learning
The futuristic car technology of tomorrow is being built today in both racing cars and
toys, writes ARTHUR GOLDSTUCK
The car of tomorrow, most of us imagine, is being built by the great automobile manufacturers of the world. More and more, however, we are seeing information technology companies joining the race to power the autonomous vehicle future.
Last year, chip-maker Intel paid $15.3-billion to acquire Israeli company Mobileye, a leader in computer vision for autonomous driving technology. Google’s autonomous taxi division, Waymo, has been valued at $45-billion.
Now there’s a new name to add to the roster of technology giants driving the future.
Amazon Web Services, the world’s biggest cloud computing service and a subsidiary of Amazon.com, last month unveiled a scale model autonomous racing car for developers to build new artificial intelligence applications. Almost in the same breath, at its annual re:Invent conference in Las Vegas, it showcased the work being done with machine learning in Formula 1 racing.
AWS DeepRacer is a 1/18th scale fully autonomous race car, designed to incorporate the features and behaviour of a full-sized vehicle. It boasts all-wheel drive, monster truck tires, an HD video camera, and on-board computing power. In short, everything a kid would want of a self-driving toy car.
But then, it also adds everything a developer would need to make the car autonomous in ways that, for now, can only be imagined. It uses a new form of machine learning (ML), the technology that allows computer systems to improve their functions progressively as they receive feedback from their activities. ML is at the heart of artificial intelligence (AI), and will be core to autonomous, self-driving vehicles.
AWS has taken ML a step further, with an approach called reinforcement learning. This allows for quicker development of ML models and applications, and DeepRacer is designed to allow developers to experiment with and hone their skill in this area. It is built on top of another AWS platform, called Amazon SageMaker, which enables developers and data scientists to build, train, and deploy machine learning quickly and easily.
Along with DeepRacer, AWS also announced the DeepRacer League, the world’s first global autonomous racing league, open to anyone who orders the scale model from AWS.
As if to prove that DeepRacer is not just a quirky entry into the world of motor racing, AWS also showcased the work it is doing with the Formula One Group. Ross Brawn, Formula 1’s managing director of Motor Sports, joined AWS CEO Andy Jassy during the keynote address at the re:Invent conference, to demonstrate how motor racing meets machine learning.
“More than a million data points a second are transmitted between car and team during a Formula 1 race,” he said. “From this data, we can make predictions about what we expect to happen in a wheel-to-wheel situation, overtaking advantage, and pit stop advantage. ML can help us apply a proper analysis of a situation, and also bring it to fans.
“Formula 1 is a complete team contest. If you look at a video of tyre-changing in a pit stop – it takes 1.6 seconds to change four wheels and tyres – blink and you will miss it. Imagine the training that goes into it? It’s also a contest of innovative minds.”
Formula 1 racing has more than 500 million global fans and generated $1.8 billion in revenue in 2017. As a result, there are massive demands on performance, analysis and information.
During a race, up to 120 sensors on each car generate up to 3GB of data and 1 500 data points – every second. It is impossible to analyse this data on the fly without an ML platform like Amazon SageMaker. It has a further advantage: the data scientists are able to incorporate 65 years of historical race data to compare performance, make predictions, and provide insights into the teams’ and drivers’ split-second decisions and strategies.
This means Formula 1 can pinpoint how a driver is performing and whether or not drivers have pushed themselves over the limit.
“By leveraging Amazon SageMaker and AWS’s machine-learning services, we are able to deliver these powerful insights and predictions to fans in real time,” said Pete Samara, director of innovation and digital technology at Formula 1.