Use case n.3: Driving into the Future: NEUROPULS and the Next Generation of Autonomous Vehicle Performance efficiency

As the automotive industry evolves, the integration of autonomous vehicles (AVs) into everyday life is expected to transform urban landscapes by reducing the need for extensive parking spaces, potentially repurposing these areas for green spaces or additional housing. This shift will contribute to the creation of more sustainable and livable urban environments. In the future, autonomous driving could lead to new business models, such as mobility-as-a-service (MaaS), where users can summon vehicles on demand, reducing the need for private car ownership and encouraging a shift towards shared transportation solutions.

The ongoing transition towards autonomous systems brings significant computational challenges and energy efficiency challenges. These systems must process vast amounts of data from various sensors, including cameras, radars, and LiDARs, in real-time to navigate complex environments safely. The capability to predict future positions and trajectories of both the AV and surrounding objects is crucial for effective path planning and collision avoidance.