Instruments For Road Network Navigation TRIZ Cases

Configurable endpoint actions help work vehicles switch to manual travel with clearer operator prompts and less time lost at route end.

Crowdsourced V2X capability data helps vehicles choose routes with stronger ADAS support when deployment is still sparse.

Area-based map update decisions use road-shape changes and vehicle actions to keep maps fresh while reducing communication load.

Real-time target feature matching and pose-based trajectory control automate geocoded data capture in hard-to-reach locations.

Unified swarm planning boosts multi-target interception while avoiding obstacles, cutting complexity versus separate allocation and routing.

Targeted UVC path planning steers articulated lamps toward mapped surfaces, cutting energy waste and harmful exposure while maintaining coverage.

A server uses map-based waypaths and vehicle status data to automate parking and fueling queues, reducing driver intervention and idle time.

Edge filtering and centralized analysis cut bandwidth use while preserving trip detection accuracy from remotely captured mobile sensor data.

Pre-generated 3D mesh and stereographic map data extend vehicle AR beyond sensor range to render occluded and distant scenes reliably.

Vehicles extract road features from onboard sensors to build map representations and identify scenarios in real time without precomputed maps.

Uses nearby vehicle types and relative positions to estimate lane boundaries when markings are obscured by weather or construction.

Sensor data is stored onboard and uploaded only when transmission is available, reducing battery drain while preserving reliable 3D data sync.

Fusing GPS, inertial sensing, and camera tracks with curve fitting improves lane-level map accuracy despite poor markings and weather.

Ground-facing optical sensing replaces slip-prone wheel-based tracking to improve driverless vehicle direction, position, and speed accuracy.

Centralized point assignment and area driving mode let multiple robots pass narrow building regions sequentially with less collision and interference.

Camera-based feature recognition fills timing-loop gaps and corrects track position continuously, even when GNSS or wheel speed is unreliable.

Perception-based inconvenience values rank pickup and drop-off points by passenger walking distance and road-edge access for safer AV stops.

Precomputed sun-position data helps autonomous vehicles reroute or reorient sensors to avoid backlight and preserve traffic light detection.

Ranks overlapping avoidance areas by routing impact so mapping teams can remap the most disruptive zones first and reduce route cost.

Combining vision, GPS, and proximity sensing improves UAV environmental maps for more reliable navigation and obstacle detection.

Magnetic markers with selective RFID tags and detection history keep vehicle positioning reliable where metal facilities disrupt GPS.

Routes are scored by item needs and road segment conditions to reduce damage while balancing delivery time, fuel use, and safety.

Moving-object SLAM is stabilized by estimating a position range from landmarks, then refining self-position with terrain maps.

Sensor-based pavement marking checks compare road markings with semantic maps, enabling fast live updates for safer autonomous routing.

By steering vehicles onto low-certainty route sections, the control system gathers sensor data to improve map accuracy without losing guidance quality.

Synchronized flood and structured IR imaging keeps mobile visual SLAM reliable in darkness and changing light while using one map for navigation.

Real-time parking data from sensors, cameras, and GPS is ranked into routes that cut search time, congestion, and driving distance.

Local image-based map node updates keep facility maps current for self-driving transport vehicles while limiting processing time and map growth.

Dynamic change-rate modeling sets map resurvey intervals by region, reducing verification burden while maintaining autonomous driving map accuracy.

3D object selection on a touch display improves agricultural vehicle autosteering precision while reducing operator monitoring and stress.

Edge devices score sensor samples by similarity and cost, sending only high-value data to improve ML coverage with less storage and compute.

Pre-assigned vehicles and ranked trip suggestions use historical user data to cut empty miles, shorten pickup times, and improve AV fleet use.

Mobile image selection helps autonomous vehicles reach precise pickup or drop-off points, reducing address ambiguity and user confusion.

Time-stamped image updates and position data let an information processor generate selectable routes for mobile body movement with less manual input.

Progressive screening of blocking obstacle polygons speeds aircraft lateral detours while preserving the preset vertical trajectory profile.

Beacon, wearable, and smart-appliance data let a server predict ride requests and send a vehicle to the pickup point with minimal user input.

A variational autoencoder predicts road feature decay stages from images, improving autonomous vehicle detection and map correlation over time.

Offline-trained recognition models stored in a high-precision map improve real-time traffic light state detection across complex driving scenes.

In-situ sensing and predictive moisture maps let harvesters adjust feed rate and speed across wet zones to reduce plugging and grain loss.

Newer autonomous driving map data supplements older navigation maps to reduce road mismatch and improve driving guidance consistency.

Fleet-level dispatch redirects autonomous vehicles for maintenance or rebalancing while still serving rides to cut downtime and preserve coverage.

Waypoint reservations with location, size, and timestamps let warehouse robots share paths safely while reducing collisions in crowded aisles.

Two LiDARs at different heights fuse static and obstacle maps to keep robot positioning accurate in dynamic environments.

Ground drones and UAVs use optimized routing and aerial servicing to farm steep terrain safely and continuously without human access.

Dividing 3D spatial data into random access units cuts transmission load while preserving selective decoding and simpler client-side structure.

New surfel measurements adjust only affected road graph segments, preserving 3D map accuracy and update speed for autonomous driving.

Camera-detected lane markers are matched with map data to correct GPS and IMU drift and localize vehicle lateral position within about 10 cm.

Motion-sensor drift is corrected with high-reliability reference positions, improving indoor and low-visibility location accuracy.

Sparse vehicle sensor readings are clustered by map tile confidence to generate faster, reliable weather warning polygons with lower compute load.

High-precision location data corrects vehicle-specific odometry drift, enabling accurate positioning when GPS or map-based localization is unavailable.