ADAS Perception Engineer – Lane Detection & Departure Warning

Recruitment Consultant

Chris Canneaux

Contact Details

ccanneaux@eu-recruit.com +44 (0)330 335 2350

Posted

14 days ago

ADAS Perception Engineer – Lane Detection & Departure Warning
• Start date: ASAP
• Contract duration: 12 months
• Working arrangement: 3 days per week in the Munich office, 2 days remote

Role Summary
Responsible for designing, implementing, and validating a camera-based lane detection and tracking system for an Advanced Driver Assistance System (ADAS). The role spans the full pipeline: image preprocessing, neural-network-based lane/marking detection, temporal tracking, lane geometry modeling, and vehicle-state fusion for warning logic.

Key Responsibilities
Analyze requirements and define the system architecture for a front-camera-based lane detection function (inputs, outputs, latency/accuracy targets, ODD — operational design domain).
Design and train a neural network (e.g., segmentation-based, anchor-based, or row-classification-based architectures such as LaneNet, SCNN, UFLD, PolyLaneNet, or transformer-based approaches) for lane marking/lane boundary detection.

Implement lane tracking across frames (Kalman filter, particle filter, or learned temporal models) to ensure stable, jitter-free lane estimates and handle occlusion, worn markings, or missing lanes.
Fit and maintain a lane geometry model (e.g., clothoid/polynomial curve fitting) and estimate vehicle position/heading relative to the lane.

Develop the Lane Departure Warning logic: time-to-lane-crossing (TTLC) estimation, threshold logic, driver intent filtering (e.g., turn signal suppression), and warning triggering strategy.
Integrate camera calibration (intrinsic/extrinsic) and perspective transformation (IPM – inverse perspective mapping) into the pipeline.

Optimize models for embedded/automotive-grade hardware (quantization, pruning, TensorRT/embedded inference frameworks) to meet real-time constraints.
Build datasets, define annotation guidelines, and drive data collection strategy for diverse conditions (rain, night, glare, worn markings, construction zones, curves).

Validate against relevant standards (e.g., Euro NCAP LDW/LKA test protocols) and define test/validation KPIs (false positive/negative rates, detection range, curvature accuracy).
Collaborate with vehicle integration teams; support HIL/vehicle-level testing.

Core technical:

Strong background in computer vision and deep learning, especially semantic segmentation, keypoint detection, or curve-fitting-based lane detection architectures.
Proficiency in Python and deep learning frameworks (PyTorch).
Solid understanding of classical CV techniques: camera calibration, homography/IPM, edge detection, Hough transforms — useful for hybrid approaches and sanity baselines.
Experience with object/lane tracking algorithms (Kalman filter, EKF, particle filters) and sensor/temporal fusion.
Familiarity with curve/polynomial or clothoid-based lane modeling.
Experience deploying models on embedded/automotive compute
C++ proficiency for production/embedded implementation.
ADAS/domain-specific:
Understanding of ADAS software architecture and real-time constraints.
Familiarity with automotive standards: Euro NCAP test protocols for LDW/LKA, ASPICE process awareness.
Experience with lane detection datasets (e.g., TuSimple, CULane, BDD100K, or proprietary OEM datasets).

Industry

Semiconductor

Contract Type

Contract

Location

Germany

City

Munich

Work Model

On-Site

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