Navigation Instruments TRIZ Cases

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See how conformal TEC modules positioned between inner and outer housings enable precise temper

See how networked smart devices detect delivery information, provide drop-off feedback, and tem

Precomputed azimuth error drift from GNSS and IMU data keeps automatic steering stable when work vehicles enter GNSS-obstructed dead reckoning.

Selective updates to entity, alignment, and location road link files cut map update time and capacity while preserving HD map consistency.

Ranks object-passing strategies and excludes unreachable space-time regions to find a globally optimal autonomous vehicle trajectory fast.

Generated lane geometries shift path centerlines away from parked vehicles and other static objects to keep autonomous navigation smooth and safe.

Branching-region control zones let a vehicle assistance system anticipate lane changes and adjust following distance more intuitively.

Occupant sensing and road-type detection let the controller personalize chassis, acceleration, and defensive driving settings in real time.

Compares observation data with a geographic model to filter redundant inputs and target only novel regions for digital map updates.

Carries destination entities across navigation and weather queries so drivers can check forecast details without repeating the location.

Real-time perception updates local maps and speed profiles so autonomous vehicles can adapt to dynamic traffic without losing smooth, safe motion.

Transforms sparse LiDAR point clouds into pillar-based pseudo-images so 2D convolutions can detect surrounding objects faster and more accurately.

Multi-sensor node matching helps mobile robots maintain route position and posture accuracy when GNSS errors distort maps.

Azimuth error-rate correction lets a work vehicle maintain stable steering during GNSS loss by compensating IMU dead-reckoning drift.

Junction control zones adapt following distance using lane-change probability, turn signals, and navigation data for smoother multi-turn driving.

Integrated inertial sensors and magnetometers track mover motion in real time to correct play-related positioning errors and detect faults early.

Frequency-domain IMU processing extracts wheel rotation and size to sustain vehicle velocity and position estimation when GNSS is unavailable.

Precomputed lattice trajectories are updated with sensor-based object costs to cut planning load while improving vehicle safety and ride comfort.

A central control unit filters proactive vehicle suggestions by occupant state and context to reduce distraction while improving comfort and safety.

Cloud processing combines vehicle, nearby traffic, and route data to guide autonomous vehicles without overloading onboard power or memory.

Real-time semantic and geometric corrections keep autonomous vehicle world models aligned with changing road features for safer navigation.

Occupancy grid segments and predicted object motion let deep RL choose vehicle actions faster while reducing sensor-processing load.

A combined occupancy grid aligns multi-sensor data across positions and times to improve visible space mapping and machine movement control.

Autonomous route generation covers two-way roads in both directions, cutting manual mission effort while improving map data accuracy.

Sensor-based signal timing, vehicle counting, and lane acceleration profiles help choose better turn lanes and routes at intersections.

By splitting AGV travel space into fixed-route and dynamic-path regions, navigation stays flexible in open areas and safe in narrow spaces.

Manual driving tracks and obstacle grid maps are combined to extract lane boundaries, traffic direction, and intersection paths for accurate autonomous maps.

Observed driveline mean and variance data refine map-based trajectory prediction, helping vehicle control account for road-user deviations.

Geometric region locks and timeout control let multiple autonomous systems update shared maps concurrently without corruption or manual scheduling.

Triggering updates ahead of road nodes keeps autonomous driving map data accurate where decisions matter while limiting storage load.

Graph attention and path-conditioned trajectory prediction replace rasterization to improve real-time vehicle planning in complex urban scenes.

Drive data is distilled into lane, edge, sign, and curb updates so vehicles get fresher navigation maps without full-map data overhead.

When vehicles block the real view, dual navigation regions switch to a lower bird's-eye route display to keep guidance visible and intuitive.

Radar point clouds generate and update local sub-maps, letting autonomous vehicles keep navigating when map server communication fails.

On-demand HD map fragments let vehicles switch between sensor-based and map-based navigation to cut bandwidth and memory use.

Displays upcoming route sections by comparing road-induced vibration parameters, helping drivers avoid discomfort and cargo damage.

Sensors detect cargo shifting in transit and trigger route or vehicle-control actions to prevent further movement and reduce manual checks.

Fusing wheel speed, visual odometry, and other vehicle sensors creates a virtual IMU that corrects bias, reduces noise, and detects IMU faults.

Smooths branch-road trajectories from HD map links to avoid rapid steering and improve passenger comfort in automated driving.

By splitting complex driving scenarios into subscenarios, goal-aware RSS derives preconditions and control strategies that preserve safety and goals.

Map-based lane and road-connection detection lets ADAS identify upcoming merges and decelerate vehicles without V2V communication.

Synchronized radar and IMU data use static landmarks and Doppler constraints to improve autonomous ground vehicle state estimation.

Analyzes lane geometry changes in precision maps to detect pocket lanes and entry or exit points, improving vehicle response and collision awareness.

Multi-vehicle object recognition updates road map confidence values while preserving hidden objects to reduce autonomous driving map errors.

Weighted fusion of sensor, map, and learned trajectory data improves driving path prediction accuracy and robustness across changing scenarios.

Independent long-term prediction and short-term motion control help autonomous vehicles react faster to moving hazards with lower processing load.

Fitted lane-line curves compare static and real-time maps to detect inconsistencies and support more reliable autonomous driving plans.

Feature point cross-checking confirms real road division line shifts, reducing false map updates from camera-only detection.

Predicted error scoring ranks traffic-scene locations for targeted data collection, improving vehicle trajectory uncertainty estimation in out-of-distribution cases.

Targeted cloud map overlays let AVs receive urgent local road changes quickly while avoiding full-map bandwidth and processing overhead.

Real-time traffic flow analysis replaces static posted limits, helping autonomous vehicles adjust speed for safer, smoother, lower-energy driving.

A highlighted vehicle function lets ambiguous short voice commands trigger the intended control faster and with fewer errors.

Switching between a fine slider and coarse buttons cuts gaze time while preserving usable value adjustment during vibration-prone vehicle operation.

Vehicles keep map data for frequently used areas and delete low-use area data to cut onboard memory needs and server communication traffic.

Virtual heading and acceleration estimates are correlated with IMU data to detect vehicle sensor misalignment and calibration drift in real time.

Onboard change detection compares sensor data with context maps to resolve road-feature mismatches during autonomous driving.

Wheel torque, rotation, and force data during steering at stops reveal road friction without separate test maneuvers, supporting safer vehicle control.

Transforms point clouds into a spherical grid to separate visible and hidden points with O(n) processing for more accurate sensor labeling.

Structured light plus omission remediation helps autonomous mobile devices detect low obstacles in blind areas and build more complete maps.

Two LiDARs at different heights separate stationary and movable objects, improving robot positioning stability and fused map accuracy.

Combining satellite, odometry, and point cloud data improves mobile robot localization by filtering unreliable inputs across diverse sites.

Strongly coupled fusion of LiDAR, image, IMU, and joint kinematics improves long-distance walking robot pose accuracy and stability.

Boundary point revision aligns dividing lines from adjoining map sections to prevent broken or deviated high-precision map displays.

A global fleet manager translates vendor-specific robot maps into one frame and uses 3D error surfaces to improve hybrid fleet navigation.

Multiple miniature radars arranged as an array enable micro robots to build 360° 3D maps in dark, narrow spaces with lower computing demand.

When a robot is outside the mapped grid, passable cells within sensor range enable path planning back to the known area for relocalization.

Operation feedback refines map detail by area, helping autonomous movable apparatuses navigate smoothly with less unnecessary map complexity.

Selective switching between two laser scanners helps a mobile robot stay localized when dynamic or unmapped zones make scan data unreliable.

Map completeness and non-contact sensing keep mining work machines on route when GPS position data is unreliable.

Onboard mission management detects environmental changes and reroutes UAVs in real time to avoid threats and conserve resources.

GPS positions and database lookups pre-label vehicle sensor data, cutting manual ADAS labeling time, cost, and human error.

Yaw rate threshold alerts and corrective pilot guidance help prevent unanticipated rotorcraft yaw in hover and low-speed flight.

Aggregated vehicle and occupant data reveals difficult road sections in real time, improving route planning and navigation safety.