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