Presentation
How Car Manufacturers Face Challenges of Urban Mobility by Changing the Way They Think and Act
Speaker
Event Type
Focus Session
AI/Machine Learning/Deep Learning
Autonomous Driving
Big Data Analytics
Clouds and Distributed Computing
TimeTuesday, June 26th11:25am - 11:50am
LocationPanorama 3
DescriptionThe automotive industry is facing a transition phase greater that anything experienced during it’s over 100 years history. Customer demand is transforming into an always present, always on, instantly available culture. At the same time emerging technologies like IoT, connected and autonomous vehicles and new players are likely to disrupt current working business model day by day. Some important factors which will influence and, finally, transform today’s world of mobility in the next decade and beyond.
1- The continuing global growth of the demand for both individual mobility and logistics leads to transport systems hitting their capacity limits more and more often. As a consequence, transport systems need to be managed more intensely than before to exploit their design capacity to an ever-higher degree. Traffic and transport strategies need to be implemented in technically and in real time as opposed to described on paper for human operators.
2- The organic growth of on-demand mobility introduces transport systems and vehicle fleets which offer new possibilities for optimizing utilization and for establishing the highest possible transport performance with the lowest possible invest in assets and infrastructure. However, the optimization problems become more and more difficult with increasing fleet sizes.
3- The transition to autonomous vehicles and autonomous operation of transport systems transforms intelligent fleet management from an option for higher efficiency and more reliability into a sine qua non. Without smart algorithms they won’t move at all.
Many of the problems which need to be solved in this context scale poorly, many of them are even NP-complete, i.e. the best solutions can only be calculated in exponential time. More often than not, however, they are required in real-time. The investment in high performance computing in traffic and transport can yield efficiency gains of the order of 10-50% compared to naïve solutions.
1- The continuing global growth of the demand for both individual mobility and logistics leads to transport systems hitting their capacity limits more and more often. As a consequence, transport systems need to be managed more intensely than before to exploit their design capacity to an ever-higher degree. Traffic and transport strategies need to be implemented in technically and in real time as opposed to described on paper for human operators.
2- The organic growth of on-demand mobility introduces transport systems and vehicle fleets which offer new possibilities for optimizing utilization and for establishing the highest possible transport performance with the lowest possible invest in assets and infrastructure. However, the optimization problems become more and more difficult with increasing fleet sizes.
3- The transition to autonomous vehicles and autonomous operation of transport systems transforms intelligent fleet management from an option for higher efficiency and more reliability into a sine qua non. Without smart algorithms they won’t move at all.
Many of the problems which need to be solved in this context scale poorly, many of them are even NP-complete, i.e. the best solutions can only be calculated in exponential time. More often than not, however, they are required in real-time. The investment in high performance computing in traffic and transport can yield efficiency gains of the order of 10-50% compared to naïve solutions.
Speaker
