Machine learning applications for the autonomous/connected vehicle : perspectives, applications and methods
The application of streaming and real-time data science/analytics to connected and autonomous vehicles is gaining traction around the world. Intelematics is an Australian leader and innovator in the field of telematics/connected vehicles as well as in big data traffic analytics, with Intelematics services used by Australian and overseas giants such as Ford, Toyota, Google etc.
The topic of the talk is the application of streaming and big data analytics to autonomous and connected vehicles. Applications covered will include : ability to predict vehicle failures, forecast traffic conditions, automate vehicle insurance claims with automated crash/incident detection, deliver data into the vehicle (traffic signal states and forecasts) etc.
The talk will give an outline of general trends as well as give some examples of concrete solutions that we have developed at Intelematics in this emerging field, both in Australia and overseas (US and EU) . The focus will be on the application of data science and algortihms, and the key role that these have to play in the emerging field of connected and autonomous vehicles. These relevant data streams bring a number of technical and non-technical challenges that will be discussed : complexity of dealing with geo-spatial and temporal data, safety and security, privacy, streaming nature of data, event-driven nature data, volume of data, complexity of relationships/patterns to be modelled etc.
The underlying algorithms and techonlogies will be also be discussed in some detail.
Link to company website :
Speaker bio :
Outline/Structure of the Talk
1.) general trends in data in the automotive industry
2.) the connected vehicle vs the autonomous vehicle
3.) examples of use-cases and technologies we have developed at Intelematics : predicting vehicle failure, real-time analytics of traffic information using GPS information from in-vehicle GPS (> 500 000 vehicles in AUS), driver risk profiling using high-resolution GPS and in-car data , traffic light forecasting
4.) the future in terms of Australian and global trends
- learn how and where machine learning is being applied in the automotive industry, i.e. connected and autonomous vehicles
- learn about statistical and mathematical challenges of applying machine learning to automotive data
- learn about some challenges that are specific to data in automotive industry
- learn about architectures deployed , i.e. IoT and cloud computing
technologists interested in application of big data in the automotive field, machine learning practitioners, data scientists, data engineers, people working at the interface of data and business/legislation
Prerequisites for Attendees
- some knowledge of machine learning basics, modelling, regression, clustering
- an interest in the application of data analytics/science to the automotive industry