Cracking the complexities of Autonomous Vehicle Development

The autonomous industry has revolutionised the personal driving experience, enhancing comfort, convenience, safety, and sustainability. Deploying autonomous vehicles poses numerous complexities and challenges development. By harnessing the power of digitalisation, we can pave the way for a safer, more efficient, and more widespread adoption of autonomous vehicles.

- Mathew Thomas

Back in 1995, Navlab5 was launched by Carnegie Mellon University on a trip from Pittsburgh to San Diego. The first self-driving car navigated itself without human intervention. So, the autonomous industry has transitioned from ‘potentially possible’ to ‘definitely possible’ to ‘inevitable’ to ‘commercially available’ now, reinventing the personal driving experience by making it more comfortable, convenient, safe, and sustainable. Undoubtedly, the industry has captured consumers' imaginations and fueled millions of investments; however, the development and deployment of autonomous vehicles come with numerous complexities and challenges. These complexities must be addressed, and the right way is through the power of digitalisation - paving the way for safer, more efficient, and widespread adoption of autonomous vehicles.

Understanding AV complexity

While the concept of self-driving cars may seem futuristic and exciting, the implementation and development of AV systems have introduced a significant amount of complexity. To achieve true self-driving capabilities, AVs require a combination of advanced technologies and engineering domains. This complexity stems from various factors, including mechanical, electrical, and electronic systems, AI, ML, and communications networks. In an AV, these domains intersect to a greater degree than they have previously, ensuring that no system operates independently.