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WirelessCar

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About us

We are the world’s leading global innovator and operator of connected vehicle services. Our vision is to lead the automotive industry into the digital society, with the goal to connect 100 million vehicles by 2025.



The car of the future is shifting from being just a product to becoming a service - customized to every user’s needs. We help create safe, smart and sustainable mobility solutions for global car brands such as Volkswagen, Volvo Cars, Jaguar Land Rover, Nissan, Subaru and Daimler.



Founded in 1999, we have continuously built upon our heritage and knowledge. Today, we are a highly recognized and award-winning company, connecting more than eight million vehicles in over 100 countries. We operate globally, headquartered in Sweden, with offices in the US and China. We are more than 600 dedicated colleagues who are committed to delivering world class services and products ranging from Safety and Security, Owner Convenience, Advanced Diagnostics, Cellular Network Integration, Over-the-Air Updates, and more.



When you join our journey, you will be part of a great crew of highly competent and warmhearted people from all over the world. You will either work in one of our customer programs with customized solutions or in our TechLab with standardized products. No matter where, your closest colleagues will be an elite agile team that together has the empowerment and responsibility to design, implement, test, and maintain the services they create.



You get the freedom to lead your own work and inspire others through a collaborative culture with high decision-making power in your team. Within our company guardrails for cybersecurity, technical hygiene, and selection of leading cloud providers you will enjoy the ability to explore and implement new technologies and techniques as you and your team see fit to best meet the needs of your customers or products working in a true DevSecOps environment. We are growing at high speed, which naturally opens up opportunities along the way, and we encourage our engineers to grow with us by following our journey. If you are a curious problem solver who thinks big, you will fit right in!



When you start working at WirelessCar, you also have a high degree of flexibility when it comes to environment and tools you get to use. We are always curious about the latest tech developments, what suits you, and what ensures a scalable and stable delivery to our customers. You choose which operating system you want to use on your laptop: Linux or Windows. You will get to work with one or more of the major cloud providers to develop services for our customers. And you will use the latest and most popular tools such as IntelliJ, Slack, Miro, Jira, Confluence and of course Teams, AD, O365.



To organize our work at scale, we follow the SAFe agile framework and subscribe to the Elite Software Performer philosophy of DevSecOps where each team is a self-contained unit fully responsible for the assets they create. We have also implemented Continuous Integration / Continuous Deployment (CI/CD) methodologies and build pipelines enabling you to create features and functions that are available in Production rapidly and reliably.

Websitehttp://www.wirelesscar.com

What we offer

  • Contribute to the automotive industry’s digital transformation
  • Strong and growing focus on facilitating the EV revolution
  • Freedom to lead your work
  • High decision-making power in your team
  • Competent and warmhearted colleagues who value diversity and the individual
  • Excellent work/life balance
  • Exclusively work with standard and cutting-edge, well documented technologies & leading global cloud providers
  • Work locally on exciting global projects with optional opportunities for travel for those interested
  • High degree of flexibility when it comes to the tech and tools you get to use
  • Breakfasts and after-works
  • Inspiring lunch talks, tech events and hackathons

News

Autonomous Mobility as a Service (MaaS)

Autonomous Mobility as a Service (MaaS), and the connected car services that will determine your success
WirelessCar
3 years ago

How does machine learning contribute to WirelessCar’s development of connected car services?

Machine learning plays an essential role in the development of connected cars and the digital services that allow these software-defined vehicles to reach their full potential. In this article, we take a closer look at WirelessCar’s work with machine learning, how that work contributes to new or improved connected car services, and the role of the OEMs in this process.What is machine learning?Machine learning is a type of artificial intelligence where computer algorithms are trained to learn from sets of data, instead of being hard coded (thus finding the right strategy from the training data). We use the datasets to train machine learning models.In “traditional programming”, a programmer will break down an issue or problem, find the underlying patterns, and instruct a program on how to solve the issue. With machine learning, we provide the model with all the relevant input, as well as the correct results we want it to predict. The model will then work out the pattern, what is causing the issue/problem, find a way for accurately predicting the results and use that to generate answers.How can machine learning improve connected car services?In the field of connected cars and connected car services, machine learning is particularly useful for data analysis. Connected cars and their sensors provide enormous amounts of data; data which can be analyzed and used to improve virtually every aspect of car ownership, fleet management, and mobility at large. Some examples:• Anomaly detection – machine learning can help identify issues by detecting unusual behavior. By training on both normal and failure patterns using DTC (Diagnostic Trouble Code) reports, it is able to identify deviations, which can result in quicker diagnoses, improved vehicle reliability, and reduced maintenance costs.• Better digital services for drivers – drivers’ preferences and user patterns (while not collected or analyzed in individual detail) can contribute to making connected car services better and more accessible; through optimized routing or improved EV charging, for example.• Safety and security – machine learning helps detect risks to driver safety and automotive cybersecurity. Maintenance needs can also be predicted and met more efficiently.WirelessCar’s data science and machine learning processesSo, what does the greater data science process look like in our case, and where does machine learning come into the picture?It all starts with the identification of a problem – an issue that needs to be solved. Depending on what the problem is, and the connected car data at our disposal, we determine which machine learning technique we should use (more on that below).Once collected, the connected car data is prepared, analyzed, and processed to fit the machine learning technique we want to use. The data and the machine learning model must be relevant to the task at hand, so that they can provide actual answers to the problems we seek to solve.The machine learning models are then put to work, a process known as training. The models receive the prepared data, find patterns, make predictions, and learn from it – getting better as they go along. Once we have a result, we analyze it and discuss with the stakeholders involved on how best to apply it in our work.Which machine learning techniques does WirelessCar use?WirelessCar currently employs all the three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.Supervised learning is used when we are trying to train a model to solve an issue using labeled data (thus data tagged with known, correct information). This is a good method to use as long as the data is of good quality and relevant to the problem we are trying to solve. A concrete example: Engine failure detection, based on diagnostic trouble codes (DTC) with known failure and reparation dates.Unsupervised learning, by contrast, is used when we need to come to a conclusion by working with unlabeled data. The machine learning model is required to analyze and find patterns from this data, such as when we try to categorize drivers into different groups according to certain parameters. We may not initially know how many groups there will be, nor how best to divide them up. Through unsupervised learning, we can find and evaluate the type of clustering/categorization that makes the most sense for our specific use-case.Then there is reinforcement learning, which means that the machine learning model learns through continuous feedback on what it does. Take autonomous driving: the model can be given rewards or penalties depending on how well it performs/”drives”. If it drives into an obstacle, it will get minus points and thus learn to avoid doing that in the future, for example.Ensuring the security and reliability of our machine learning workWirelessCar has chosen to work with Amazon Web Service (AWS) as our data science and machine learning partner. Our partnership with AWS goes back a long time, and our data is stored in AWS’s secure cloud services. We use Amazon SageMaker and Redshift for our machine learning models and QuickSight for visualization.It is crucial to stress that the data we analyze is anonymized. We do not have any traceable user information (drivers’ social security numbers, vehicle numbers, etcetera) and thus cannot use it in our machine learning. Additionally, our machine learning work does not involve that kind of granular breakdown of data. We are interested in the bigger picture, and in creating products and solutions that are useful for all OEMs, in all their markets.Overcoming the challenges of machine learning, together with OEMsWhat are the main challenges of machine learning, at least in this context? How can we overcome these challenges, and deliver better connected car services for OEMs?1. Connected car data collection and analysisEssentially, data quantity is the key to connected car service quality. The more connected car data you can work with, the better analyses you will be able to make through your machine learning processes. Not all data will be relevant for a particular machine learning project, but as connected car services inevitably become more interconnected and complex, it is critical to have access to as much data as possible when developing these services.2. Handling big data volumesEven comparatively small data sets can concern millions of vehicles, so handling enormous amounts of data is highly complex. At WirelessCar, we make sure to only invest in and work with the kind of technology that can live up to these demands, and that provides us with the tools necessary for big data management. That is a prerequisite for continuously enhancing our machine learning, and developing our connected car services for (and alongside) OEMs.3. Collaborating closely with OEMsThe closer the collaboration between us and the OEM, the better the results. Rarely is this more true than in the case of machine learning. We need to understand our customers’ needs and problems (and their customers’, in turn), and train our machine learning models accordingly. Through this collaborative process, not only are we able to overcome multifaceted issues together, but better prepare for the future as well.
WirelessCar
3 years ago
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