TLDR High-definition (HD) maps are essential for self-driving cars, but their reliability degrades as real-world conditions change due to construction, accidents, road wear, and weather. This misalignment between static maps and dynamic environments impacts safety, rider trust, and fleet efficiency. To solve this, we propose a scalable, systematic framework that leverages fleet vehicle cameras and sensors to detect new or changed road features in real time, delivering over-the-air (OTA) updates to the entire fleet. By improving map accuracy, coverage, and responsiveness to dynamic events, the solution reduces safety risks, minimizes sharp maneuvers and false braking, ensures accurate ETAs, and enhances the overall rider experience—advancing the mission of building safe, reliable autonomous vehicles for everyone, everywhere. Problem Design a systematic approach by which the AV Map can be maintained/updated to make the Maps more reliable for self-driving cars Background HD maps (high definition maps) are essential for self-driving cars and they have a high accuracy of object locations, up to 5 cm. These maps solve the problem of localization and can provide valuable information on objects like traffic lights, drivable lanes, bicycle lakes, road signs, the speed limit, pedestrian crossings and curbs etc., which is essential for path planning. The most significant problem with the HD map is the change in the physical features with construction, new static objects, road wear and tear etc. The change of physical feature can give the incorrect environment information to the autonomous driving system, and it can cause a negative impact on the safety and rider experience of autonomous driving vehicles. The localization based on the alignment of landmark can also suffered from degradation of the accuracy and reliability due to a misalignment between maps and the real world. Therefore, the changes of HD map must be detected and managed effectively and at scale to allow the autonomous driving vehicles to operate safely. Solving this problem aligns with the product strategy to build safe and reliable self-driving cars for everyone and everywhere Goals Design a systematic approach by which the AV Map can be managed and updated effectively taking into account physical features such as construction, new static objects, road wear and tear, impact of weather on road etc. The approach must be scalable, must manage real-time updates, must create a better rider experience for self-driving cars and mitigate impact on fleet operations. Metrics to Measure Success Improving Map Coverage and Reliability Reduce %age of unclassified objects on road Increase %age identification of new static objects on roadIncrease %age identification of moved/removed static objects Increase %age identification of road wear and tear High confidence identification of dynamic road scenarios like construction, collision, potholes etc. Improving Map Coverage and Reliability Reduce %age of unclassified objects on road Increase %age identification of new static objects on road Increase %age identification of moved/removed static objects Increase %age identification of road wear and tear High confidence identification of dynamic road scenarios like construction, collision, potholes etc. Improving Rider Experience Reducing near miss incidentsReducing sharp maneuversReducing false emergency brakingReducing manual interventionsMeeting ETA Improving Rider Experience Reducing near miss incidents Reducing sharp maneuvers Reducing false emergency braking Reducing manual interventions Meeting ETA Market Analysis Based on my short survey on internet online the top 4 reasons of improving rider experience for autonomous vehicle (AV). Visibility into AV’s perceptionVisibility into AV’s short term and long-term decisionsAbility to pullover in case of hardware or software failureNo incidents during tripGetting to destination on time Visibility into AV’s perception Visibility into AV’s short term and long-term decisions Ability to pullover in case of hardware or software failure No incidents during trip Getting to destination on time Assumption: My survey is representative of the feedback of general public feedback Based on data analysis for US department of transportation and NHSTA data the 3rd cause of road fatality after, speeding and DUI is work zone fatalities. So, identifying and updating AV maps with the dynamic objects in work zone is absolutely critical for safety of the people both inside and outside the vehicle. Here are some of the scenarios that needs to be identified and updated on the AV maps for the benefit of the AV fleet Detour/Road closure updatesConstruction zone updatesPotholes, road wear and tearAccident or disabled vehicles on roadSnow or ice on road Detour/Road closure updates Construction zone updates Potholes, road wear and tear Accident or disabled vehicles on road Snow or ice on road Assumption: The data set is based on year 2016 and the data distribution is similar for 2019 User Personas Features/Hypotheses for MVP to explore GoalFeature IdeaPriorityNotesImproving Map Reliability and CoverageFleet camera based dynamic event detection for Detour/Road closure updates/Construction zonesHighCreate a Map updates options where fleet drivers can use camera on their mobile for perception of dynamic events on road.Fleet camera based dynamic object detection for new/ moved/ deleted static road objects  HighFleet camera based dynamic event detection for accidents/disabled vehicles/emergency vehiclesMediumFleet camera based dynamic object detection for potholes/ road wear and tearMediumProvide OTA event updates over 5G to AV fleetImproving Rider ExperienceReal time OTA event updates to AV fleetHighVisibility into AV’s path plan with dynamic event updates and re-route options for ridersHighCreate a visualization of path plan of and overlay event updates from fleet vehiclesReal-time camera feed and simulation options for vehicle operators/ validation of motion planningMediumCreate real time simulation of AV’s response to dynamic event updates and provide re-route options. Use driver’s reaction to dynamic events as a validation for AV’s response. GoalFeature IdeaPriorityNotesImproving Map Reliability and CoverageFleet camera based dynamic event detection for Detour/Road closure updates/Construction zonesHighCreate a Map updates options where fleet drivers can use camera on their mobile for perception of dynamic events on road.Fleet camera based dynamic object detection for new/ moved/ deleted static road objects  HighFleet camera based dynamic event detection for accidents/disabled vehicles/emergency vehiclesMediumFleet camera based dynamic object detection for potholes/ road wear and tearMediumProvide OTA event updates over 5G to AV fleetImproving Rider ExperienceReal time OTA event updates to AV fleetHighVisibility into AV’s path plan with dynamic event updates and re-route options for ridersHighCreate a visualization of path plan of and overlay event updates from fleet vehiclesReal-time camera feed and simulation options for vehicle operators/ validation of motion planningMediumCreate real time simulation of AV’s response to dynamic event updates and provide re-route options. Use driver’s reaction to dynamic events as a validation for AV’s response. GoalFeature IdeaPriorityNotesImproving Map Reliability and CoverageFleet camera based dynamic event detection for Detour/Road closure updates/Construction zonesHighCreate a Map updates options where fleet drivers can use camera on their mobile for perception of dynamic events on road.Fleet camera based dynamic object detection for new/ moved/ deleted static road objects  HighFleet camera based dynamic event detection for accidents/disabled vehicles/emergency vehiclesMediumFleet camera based dynamic object detection for potholes/ road wear and tearMediumProvide OTA event updates over 5G to AV fleetImproving Rider ExperienceReal time OTA event updates to AV fleetHighVisibility into AV’s path plan with dynamic event updates and re-route options for ridersHighCreate a visualization of path plan of and overlay event updates from fleet vehiclesReal-time camera feed and simulation options for vehicle operators/ validation of motion planningMediumCreate real time simulation of AV’s response to dynamic event updates and provide re-route options. Use driver’s reaction to dynamic events as a validation for AV’s response. GoalFeature IdeaPriorityNotes Goal Goal Goal Feature Idea Feature Idea Feature Idea Priority Priority Priority Notes Notes Notes Improving Map Reliability and CoverageFleet camera based dynamic event detection for Detour/Road closure updates/Construction zonesHighCreate a Map updates options where fleet drivers can use camera on their mobile for perception of dynamic events on road. Improving Map Reliability and Coverage Improving Map Reliability and Coverage Improving Map Reliability and Coverage Fleet camera based dynamic event detection for Detour/Road closure updates/Construction zones Fleet camera based dynamic event detection for Detour/Road closure updates/Construction zones Fleet camera based dynamic event detection for Detour/Road closure updates/Construction zones High High High Create a Map updates options where fleet drivers can use camera on their mobile for perception of dynamic events on road. Create a Map updates options where fleet drivers can use camera on their mobile for perception of dynamic events on road. Create a Map updates options where fleet drivers can use camera on their mobile for perception of dynamic events on road. Fleet camera based dynamic object detection for new/ moved/ deleted static road objects  High Fleet camera based dynamic object detection for new/ moved/ deleted static road objects Fleet camera based dynamic object detection for new/ moved/ deleted static road objects Fleet camera based dynamic object detection for new/ moved/ deleted static road objects High High High Fleet camera based dynamic event detection for accidents/disabled vehicles/emergency vehiclesMedium Fleet camera based dynamic event detection for accidents/disabled vehicles/emergency vehicles Fleet camera based dynamic event detection for accidents/disabled vehicles/emergency vehicles Fleet camera based dynamic event detection for accidents/disabled vehicles/emergency vehicles Medium Medium Medium Fleet camera based dynamic object detection for potholes/ road wear and tearMediumProvide OTA event updates over 5G to AV fleet Fleet camera based dynamic object detection for potholes/ road wear and tear Fleet camera based dynamic object detection for potholes/ road wear and tear Fleet camera based dynamic object detection for potholes/ road wear and tear Medium Medium Medium Provide OTA event updates over 5G to AV fleet Provide OTA event updates over 5G to AV fleet Provide OTA event updates over 5G to AV fleet Improving Rider ExperienceReal time OTA event updates to AV fleetHigh Improving Rider Experience Improving Rider Experience Improving Rider Experience Real time OTA event updates to AV fleet Real time OTA event updates to AV fleet Real time OTA event updates to AV fleet High High High Visibility into AV’s path plan with dynamic event updates and re-route options for ridersHighCreate a visualization of path plan of and overlay event updates from fleet vehicles Visibility into AV’s path plan with dynamic event updates and re-route options for riders Visibility into AV’s path plan with dynamic event updates and re-route options for riders Visibility into AV’s path plan with dynamic event updates and re-route options for riders High High High Create a visualization of path plan of and overlay event updates from fleet vehicles Create a visualization of path plan of and overlay event updates from fleet vehicles Create a visualization of path plan of and overlay event updates from fleet vehicles Real-time camera feed and simulation options for vehicle operators/ validation of motion planningMediumCreate real time simulation of AV’s response to dynamic event updates and provide re-route options. Use driver’s reaction to dynamic events as a validation for AV’s response. Real-time camera feed and simulation options for vehicle operators/ validation of motion planning Real-time camera feed and simulation options for vehicle operators/ validation of motion planning Real-time camera feed and simulation options for vehicle operators/ validation of motion planning Medium Medium Medium Create real time simulation of AV’s response to dynamic event updates and provide re-route options. Use driver’s reaction to dynamic events as a validation for AV’s response. Create real time simulation of AV’s response to dynamic event updates and provide re-route options. Use driver’s reaction to dynamic events as a validation for AV’s response. Create real time simulation of AV’s response to dynamic event updates and provide re-route options. Use driver’s reaction to dynamic events as a validation for AV’s response. Assumption: The fleet is available anywhere we need to maintain the map we have an active fleet of vehicles User interaction and design Feature 1: Fleet camera based dynamic event detection for Detour/Road closure updates/Construction zones Feature 2: Fleet camera based dynamic object detection for new/ moved/ deleted static road objects Feature 3: Fleet camera based dynamic event detection for accidents/disabled vehicles/emergency vehicles Feature 4: Fleet camera based dynamic object detection for potholes/ road wear and tear Feature 5: Real time OTA event updates to AV fleet Feature 6: Visibility into AV’s path plan with dynamic event updates and re-route options for riders Feature 4: Real-time camera feed and simulation options for vehicle operators Feature 5: driver fleet data for training / validation of motion planning Hypotheses Testing and Performance Measurement

Systematic Approach to Reliable HD Map Maintenance for Self-Driving Cars

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