Artificial Intelligence Engineer
An AI Engineer in the automotive industry has the responsibility of developing and implementing artificial intelligence (AI) and machine learning (ML) solutions to enhance various aspects of automotive technology. The automotive industry is increasingly adopting AI-driven technologies to improve vehicle safety, performance, efficiency, and user experience.
keys Roles and Responsibility
Autonomous Driving Systems: Developing AI algorithms and models for autonomous driving systems, including perception, decision-making, and control modules that enable vehicles to navigate and drive safely without human intervention.
Sensor Fusion: Integrating data from various sensors, such as cameras, LiDAR, radar, and ultrasonic sensors, to provide a comprehensive view of the vehicle's surroundings for autonomous driving and advanced driver assistance systems (ADAS).
ADAS Development: Designing and implementing AI solutions for Advanced Driver Assistance Systems, such as lane-keeping assist, adaptive cruise control, automated emergency braking, and blind-spot monitoring.
Natural Language Processing (NLP) and Human-Machine Interaction: Developing AI models for natural language understanding and voice-based interactions in infotainment systems and voice assistants within the vehicle.
Computer Vision Applications: Creating AI algorithms for computer vision tasks, like object detection, recognition, and semantic segmentation, used for pedestrian detection, traffic sign recognition, and other safety features.
Data Collection and Annotation: Collaborating with data engineers to collect and preprocess large datasets for training and validating AI models. Conducting rigorous testing and validation of AI algorithms and systems to ensure their robustness and reliability in various scenarios.
Model Training and Optimization: Training machine learning models using various techniques, such as deep learning and reinforcement learning, and optimizing them to achieve high accuracy and efficiency.
Inference and Real-Time Processing: Ensuring that AI models are optimized for real-time processing and deployed effectively in automotive embedded systems.
Functional Safety: Ensuring that AI systems comply with functional safety standards (e.g., ISO 26262) to guarantee the safe operation of AI-driven automotive features. Implementing measures to safeguard sensitive data and ensure compliance with data privacy regulations in AI applications.
Technical Skills
Introduction to Self Driving Car
Understanding Electric Vehicles Technology
Gaussian Processes & Bayesian Optimization
LIDAR for ADAS and Autonomous Sensing
Sensor Fusion and Non-linear Filtering for Automotive Systems