ADAS Perception Engineer – Computer Vision / Autonomous Driving / Embedded AI

European Tech Recruit; München; Vor Ort; EUR 70.000 - 100.000; Vollzeit

Zusammenfassung

European Tech Recruit is seeking an ADAS Perception Engineer to design, develop, and validate camera-based lane detection and LDW systems for production vehicle programmes.

The role focuses on perception architecture, DL model development with thinking about latency, ODD targets, and robust lane tracking using Kalman filters, EKF, particle filters, or learned models. Strong Python/PyTorch, CV knowledge, and automotive safety familiarity are required.

Qualifikationen

• 3–6 years of commercial experience developing computer vision and perception systems for ADAS or autonomous driving.
• Expertise in deep learning techniques such as semantic segmentation, object detection, keypoint detection, or lane detection.
• Proficient in Python and PyTorch for ML model development.
• Solid understanding of classical CV techniques including camera calibration, homography, IPM, edge detection, and Hough transforms.
• Experience implementing tracking algorithms including Kalman Filters, EKF, particle filters, or sensor fusion.
• Knowledge of lane modelling techniques including polynomial fitting, clothoid modelling, or geometric curve estimation.
• Experience with Lane Departure Warning (LDW), Lane Keeping Assist (LKA) or related ADAS perception systems.
• Familiar with ASPICE and automotive functional safety processes.

Aufgaben

• Design and develop camera-based lane detection and tracking systems for Advanced Driver Assistance Systems (ADAS).
• Analyse system requirements and define perception architecture, including latency, accuracy, and ODD targets.
• Develop, train, and optimise deep learning models for lane marking and lane boundary detection using segmentation, keypoint detection, or transformer-based architectures.
• Implement robust lane tracking algorithms using Kalman Filters, Extended Kalman Filters (EKF), particle filters, or learned temporal models.
• Develop lane geometry modelling algorithms using polynomial, clothoid, or curve-fitting techniques to estimate vehicle position within the lane.

Kenntnisse

Computer vision

Perception systems

ADAS

Deep learning

Python

PyTorch

Kalman Filters

Sensor fusion

Lane detection

Lane Departure Warning

Tools

TensorRT

C++

Jobbeschreibung

ADAS Perception Engineer – Computer Vision / Autonomous Driving / Embedded AI

We are currently partnered with an innovative automotive technology company developing next-generation Advanced Driver Assistance Systems (ADAS) for intelligent and autonomous vehicle platforms. As part of their continued investment in perception technologies, they are looking to hire an ADAS Perception Engineer to design, develop, and validate advanced camera-based lane detection and Lane Departure Warning (LDW) systems for production vehicle programmes.

Key responsibilities

• Design and develop camera-based lane detection and tracking systems for Advanced Driver Assistance Systems (ADAS).
• Analyse system requirements and define perception architecture, including latency, accuracy, and Operational Design Domain (ODD) targets.
• Develop, train, and optimise deep learning models for lane marking and lane boundary detection using semantic segmentation, keypoint detection, or transformer-based architectures.
• Implement robust lane tracking algorithms using Kalman Filters, Extended Kalman Filters (EKF), particle filters, or learned temporal models.
• Develop lane geometry modelling algorithms using polynomial, clothoid, or curve-fitting techniques to accurately estimate vehicle position within the lane.

Key requirements

• 3-6 years of commercial experience developing computer vision and perception systems for ADAS or autonomous driving applications.
• Proven expertise in deep learning techniques including semantic segmentation, object detection, keypoint detection, or lane detection architectures.
• Strong programming skills using Python and PyTorch for machine learning model development.
• Solid understanding of classical computer vision techniques including camera calibration, homography, inverse perspective mapping (IPM), edge detection, and Hough transforms.
• Experience implementing tracking algorithms including Kalman Filters, Extended Kalman Filters (EKF), particle filters, or sensor fusion techniques.
• Knowledge of lane modelling techniques including polynomial fitting, clothoid modelling, or geometric curve estimation.
• Experience developing Lane Departure Warning (LDW), Lane Keeping Assist (LKA), or broader ADAS perception systems.
• Knowledge of automotive development processes including ASPICE and functional safety standards.

Keywords

ADAS Perception Engineer / Computer Vision Engineer / Autonomous Driving / ADAS / Lane Detection / Lane Departure Warning / LDW / Lane Keeping Assist / LKA / Perception Engineer / Deep Learning / Machine Learning / Computer Vision / Semantic Segmentation / LaneNet / SCNN / UFLD / PolyLaneNet / Transformer Models / PyTorch / Python / C++ / Embedded AI / TensorRT / Camera Calibration / Inverse Perspective Mapping / IPM / Kalman Filter / Extended Kalman Filter / EKF / Particle Filter / Sensor Fusion / Polynomial Curve Fitting / Clothoid Modelling / Euro NCAP / ASPICE / Automotive Software / Embedded Systems / TuSimple / CULane / BDD100K / Vehicle Perception

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