Shekhar Raheja

Responsibilities as a Project Manager: • Key contact person for various internal/external stakeholders for project activities and deliverables. • Led cross-functional/cross-organizational critical topics towards SOP-readiness. • Proactively tracked and communicated progress, deviations and engineering needs to higher management. Supplier management: • Negotiated with Tier-1 supplier teams on scope and timeline of deliverables. • Contributed to RFI & RFQ discussions on projects of strategic importance.

• Central point-of-contact for Lidar-based ADAS/AD feature development. • Responsible for overall functional and performance readiness of the function at any point in the product life cycle. • Transformed business requirements into technical solutions. • Definition of system level KPIs and performance validation. • Root-cause analysis at system level for issues reported from test drives. • Documented function design, technical limitations, risk assessment and impact analysis.

Part of team working on design and development of Lidar-based advance driver-assistance functions. Responsibilities include: -- Algorithm design & documentation -- Software development -- Requirements analysis -- Module high level and low level design -- Static code analysis SW Tools/Technologies: -- C++ (OpenCV), Python -- Git, Dimensions -- Doors -- MS Office (Word, Excel, PowerPoint) -- G Suite -- Klocwork

Development of "Pedestrian Intention Recognition" algorithms in the domain of video-based advanced driver assistance systems. The system performs pedestrian action classification and predicts whether the pedestrian ahead will cross the road or stop. Augmented features consisting of head-pose orientation over time and motion features in 3D-space are fed into the system. Action classification is based on time-series analysis and probabilistic trajectory matching within a particle-filtering framework.

Developing "Pedestrian Intention Recognition" algorithms in the domain of video-based advanced driver assistance systems. The system predicts whether the pedestrian ahead will cross the road or not based on time-series analysis and probabilistic trajectory matching.

Research assistant in Machine Vision lab working on Real-time hand gesture recognition at CSIR-CEERI, Pilani, India.

Computer Vision: Object Tracking | 3D Computer Vision | Machine Learning | Digital Image Processing | Human-Comuter interaction | Data Mining | Information Visualization & Visual Analytics | Human Centered Robotics | Real Time Systems