Conclusion and Reference (Neural networks in the automotive sector)

 

In conclusion, vehicles have all the necessities for driving except for a brain to be able to drive by itself and providing a vehicle with one might eradicate the need for a driver, however this seems to be a very difficult task, though the potential of driverless cars is increasing and at the horizon of becoming a reality with the implementation of more data gathering from current NNs and improved cameras and sensors.

 

 

 

Reference:

 

Cadence, 2021. Neural networks in the automotive world. [Blog] Cadence PCB solutions, Available at: <https://resources.pcb.cadence.com/blog/neural-networks-in-the-automotive-world-2> [Accessed 24 April 2021].

Hernandez, C. (2017) ‘Deep Learning Neural Networks for Self-Driving Cars’  [online] linkedin.com. Available at:< https://www.linkedin.com/pulse/deep-learning-neural-networks-self-driving-cars-claudio-hernandez> [Accessed 29 April 2021].

IBM, 2021. What are Recurrent Neural Networks?. [online] Ibm.com. Available at: <https://www.ibm.com/cloud/learn/recurrent-neural-networks> [Accessed 29 April 2021].

Luckow, M. et al. (2016) ‘Deep learning in the automotive industry: Applications and tools’ IEEE International Conference on Big Data (Big Data), pp. 3759-3768. doi: 10.1109/BigData.2016.7841045.

NetApp, 2021. Artificial Intelligence in the Automotive Industry. [Blog] NetApp, Available at: <https://blog.netapp.com/artificial-intelligence-in-the-automotive-industry/> [Accessed 27 April 2021].