Topics:
- Robust path generation and decision-making leveraging AI approaches
- Algorithms: Integrating AI methods and rule-based approaches for enhanced path planning and decision-making for ADAS and AD
- Tools: Training and validation tooling for AI-based system designs for path planning and decision-making
- Generative AI and multi modal large language models
- Algorithms: Enhancing the capabilities and safety of AD and ADAS software
- Software Development: Enhancing software development, deployment and validation and verification approaches.
- Federated Learning and Computing
- Learning: Leveraging unlabeled data from the vehicle fleet to efficiently apply semi/self-supervised learning at edge devices
- Computing: Design of efficient, low-latency and high-throughput data processing in resource-constrained environments
I prefer tracks 1 and 3 Track 1 because:
- I have good knowledge on path-planning algorithms (A*, RRT, Dijkstra’s)
- I have taken courses on machine learning, reinforcement learning, computer vision, introduction to robotics and they all tie together
Zenseact
- AI for autonomous driving
- ADAS
Zenseact - Company notes Zenseact - Possible Interview Questions
Topic 1
Path planning AI rule-based approaches Decision Trees Learning Classifier Systems Association Rule Learning Artificial Immune Systems