Design and Debug an Automotive Sensor and Camera-Blockage System

# Design and Debug an Automotive Sensor and Camera-Blockage System You are given a set of driving capabilities for a vehicle. Explain how you would choose sensor types and placement to support those capabilities. Then analyze external camera failure modes and design a feature that reports camera blockage. As a debugging case study, the blockage signal produces a false positive about once per hour. Explain how you would communicate with a technician and guide the quickest safe investigation. The exact driving capabilities, vehicle geometry, operating domain, and safety target are not specified. Ask for them rather than assuming a complete autonomous-driving scope. ### Clarifying Questions to Ask - Which driving capabilities and operational design domain must the vehicle support? - What weather, lighting, speed, road, geographic, and regulatory conditions apply? - What detection range, field of view, redundancy, diagnostic coverage, and cost constraints matter? - What does “blocked” mean, how quickly must it be detected, and what vehicle behavior follows the alert? - Does the hourly false positive correlate with location, weather, speed, cleaning, startup, or another event? ### Part 1 — Choose sensors and placement Map each required driving capability to measurable perception needs, then choose sensor modalities, fields of view, overlap, and mounting locations. #### What This Part Should Cover - Requirement-to-sensor traceability rather than a generic sensor shopping list. - Range, resolution, field of view, occlusion, cleaning, thermal, vibration, and calibration constraints. - Overlap and diversity for fault detection, along with compute, bandwidth, cost, and serviceability trade-offs. ### Part 2 — Detect camera blockage and external failures Identify environmental and physical failure modes and design a blockage health signal with clear confidence and response semantics. #### What This Part Should Cover - Dirt, water, ice, glare, darkness, fog, obstruction, lens damage, misalignment, vibration, and thermal effects. - Spatial, temporal, and cross-sensor features that distinguish blockage from a valid low-texture scene. - Calibration, thresholds, hysteresis, uncertainty, fault isolation, and degraded-mode behavior. ### Part 3 — Diagnose an hourly false positive Give a technician a fast, safe, evidence-preserving workflow that can reproduce or isolate the issue before components are replaced. #### What This Part Should Cover - A concise symptom definition and exact evidence to capture around each trigger. - Separation of sensor, harness, cleaning, calibration, environment, model, timing, and software causes. - Safe reproduction steps, comparison tests, escalation criteria, and feedback into engineering. ### What a Strong Answer Covers - Derives the design from capabilities and the operating domain. - Treats a blockage detector as a safety-relevant diagnostic with measurable false-positive and false-negative costs. - Uses redundancy without assuming another sensor is always correct. - Turns the technician conversation into a bounded decision tree supported by timestamps, raw evidence, and configuration identity. ### Follow-up Questions 1. How would you validate blockage detection for rare weather without collecting unsafe on-road failures? 2. What should the vehicle do when two forward cameras disagree about blockage? 3. How would you detect that a camera is physically shifted but still producing sharp images?