ADAS Perception Engineer – Lane Detection & Departure Warning - Munich - Long Term Contract

ADAS Perception Engineer – Lane Detection & Departure Warning Initial 12 month freelance contract + potential extensionsMunich (3 days per week onsite / 2 days remote) ASAP start 40 hours/weekMin 4-5 years experienceRole SummaryResponsible 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.Required Skills & ExperienceCore 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).