Deep learning algorithms and data schemes

Self-supervised and behavior learning for AVs

Deep neural and convolutional networks / deep learning and neural networks: Challenges for decision-making algorithms in heavy traffic, self-driving vehicles

Data processing and AI: software architecture and hardware challenges

Path planning and object recognition with AI / future pathways, required research and the role of deep learning in the mix of computer vision approaches

Tools for enabling deep learning systems

Full stack software suites for AI in ADAS providing hardware agnostic, scalable solutions

Deep learning for human-centered semi-autonomous driving

Role of cognitive computing systems and deeper direct perception in autonomous driving in Level 4 and 5 cars

Artificial reality? Deep learning with synthetic data from driving simulations

Algorithms for cameras as primary sensors for accomplishing the tasks of object recognition and classification, localisation, decision making, trajectory planning and vehicle control

Visual processing to ADAS: applications, architectures and algorithms

Deep learning with multi-sensor data and self-healing map for automated driving

Imaging vision in automotive linked cameras/ISP: processing chain, algorithms and camera systems architecture

How neural nets can leverage domain-specific knowledge in computer vision

Sensor fusion deep learning architectures

Automotive camera technology and computer vision algorithms

Collaborative sensor fusion to improve sensing of the fused system

Neural networks in sensors

Multi-core processing approaches for AI-driven autonomous vehicles

Software architectures for AI and deep driving