Innovation and collaborative, synchronized program management for new programs
Popularly known as self-driving cars, autonomous vehicles are automobiles that can operate and navigate with minimal human input by leveraging various integrated technologies, including but not limited to: artificial intelligence, sensors, big data, IoT connectivity, and cloud computing. While still in its early stages, autonomous vehicle technology is considered to be a disruptive innovation that will rapidly revolutionize the entire transportation industry in the coming years.
From the perspective of design engineers and automotive manufacturers, autonomous vehicles are software-intensive mechatronic systems integrating more than a hundred electronic control units (ECUs) connected through various types of networks. Fully-functional autonomous driving systems will require some of the most complex software implementations that carmakers have ever faced – combining a variety of data feeds (e.g. information from sensors, traffic data from the cloud, data coming from other vehicles or infrastructure), and tying it all into the vehicle's electronic and mechanical components to create a network of onboard systems that all work together reliably without user input or correction.
To address the challenges of autonomous vehicle development going forward, automotive companies must rethink the traditional product lifecycle and adopt an engineering methodology that utilizes agile, model-based development with integrated data flows (i.e. digital threads) and robust software-based simulation capabilities.