Instruments For Road Network Navigation TRIZ Cases

By combining occupant classification, destination data, and travel conditions, the vehicle times HMI and audio actuation to wake riders appropriately.

Road quality data normalizes driving events so driver scores reflect actual behavior instead of potholes, uneven surfaces, or other road conditions.

Predicted vehicle weight and center of gravity are used to correct motion requests, stabilizing travel under changing cargo and occupant loads.

Color-coded lane guidance marks where autonomous mode is allowed and shows remaining lane distance to support safer driver decisions.

Fused map and sensor data enable lane-level route updates and adaptive lane-keeping across adjacent lanes in changing road conditions.

Fusing sensor, perception, and onboard map data with actor-based reasoning updates road semantics in real time despite occlusions and weather.

Fused camera, radar, and other sensor data builds a unified environmental map that improves object, route, and navigation visualization.

Route and vehicle data are combined to build speed profiles that guide drivers and cut energy use on specific route segments.

Motion-based prediction and correction of road marking state vectors reduces noise sensitivity and improves guidance input reliability.

Ranks vehicle subgroups by carbon footprint and uses lower-emission driving patterns to guide behavior changes securely.

3D path projections let autonomous vehicles ignore nonblocking objects and keep moving safely through narrow aisles and doorways.

Pre-surveyed geofences and waypaths let autonomous vehicles park and retrieve passengers reliably in complex urban parking areas.

Road surface and travel data from preceding vehicles help set safe speed through danger zones with more accurate control in hazardous conditions.

Crowdsourced telemetry and map geometry estimate intersection turn difficulty, helping trailer-towing vehicles choose routes with enough time and space.

Position-based alerts estimate when a driver should return to an EV charger, helping avoid blocking fees and free stations sooner.

AI monitors occupant behavior and driving risk, then shifts from occupant assist to driving assist before unsafe conditions escalate.

Overhead rails and a movable charging network let electric buses charge in narrow, irregular garage layouts without extra vehicle repositioning.

Region-based camera settings adjust frame rate and pixel decimation by driving and weather conditions to improve road line and vehicle detection.

Route and charging-station data trigger timely battery warming before arrival, cutting low-temperature EV charging delays.

A bounded local graph with candidate lanes and lane references lets vehicles reroute around blockages while limiting processing load.

Nearby EVs are matched by location, charge level, route, and availability to deliver rescue power when a stranded vehicle cannot reach a charger.

A linear battery icon shows nominal and dynamic EV range together, reducing confusion from fluctuating real-time range estimates.

Map-based lane-link selection minimizes crossing paths at tollgates, enabling safer hands-off travel through non-lane sections.

Multiple candidate trajectories are scored against driver, vehicle, and road conditions to choose a drivable path with fewer unpleasant automated maneuvers.

Weather-aware route guidance steers EVs away from low-temperature zones and plans charging stops to avoid battery drain and delivery delays.

Driving-situation-based support point selection enables precise lateral guidance from external map data while limiting onboard memory and computing load.

Multiple-antenna RF and CSI amplitude analysis identify which side a rider is on when GPS is unreliable, enabling faster, lower-idle pickup.

When an autonomous vehicle gets stuck, segmented sensor data helps an assistance center return a revised trajectory with less delay.

When lane markers disappear or road geometry is hard to read, virtual lane generation uses prior lane and vehicle data to maintain path control.

A trained initializer guides constrained trajectory optimization to avoid local optima and deliver real-time mobile robot paths under hard constraints.

A mixture model blends steady-state and transient motion models to keep sideslip and inertial positioning accurate during rapid steering changes.

Distance discontinuity checks help reject false lane-marking detections on poor roads, improving trajectory control reliability and ride comfort.

Automatic trajectory planning replaces manual driving to collect higher-quality calibration data faster for cameras and radar in autonomous vehicles.

Plans when and where electric delivery vehicles charge using route, battery, delivery rules, and station energy to avoid delivery delays.

A context-sensitive vehicle interface combines map and autonomous views while keeping key driving information visible as controls adapt.

Satellite images and machine learning estimate route solar exposure, warn of vehicle deterioration, and support lower-radiation travel paths.

Routes EVs to charging stations used by similar drivers, using shared preference data to cut travel time and improve charging convenience.

Occupant-aware failure alerts suppress non-urgent notifications for non-participants while preserving timely repair action within vehicle usage periods.

Spatial-temporal graph planning uses road-aligned virtual nodes and kinematic constraints to generate smoother trajectories around real-time obstacles.

Predicted route and sensor FOV data reveal blind areas in AR, helping drivers anticipate visibility gaps and adjust autonomous driving modes.

Multiple magnetic sensors use time-difference averaging and vibration frequency data to suppress road-marking noise and improve driving information.

Route order is optimized by item mass and destination sequence to cut energy use, preserve range, and reduce delivery trips.

Detailed map-based speed planning defines merge start and completion points to avoid abrupt acceleration or deceleration at highway merges.

Map road references are matched to sensor-captured features through polyline deviation analysis to improve vehicle pose accuracy when GNSS is noisy.

Real-time inventory, location, and processing data are scored to reroute fleet vehicles faster and avoid stale, random delivery decisions.

By comparing route turn data with current lane direction, this case guides autonomous lane changes near intersections with less processing load.

When no route is selected in time, onboard control uses validated off-board route data to slow or stop the vehicle and avoid unsafe entry.

By counting nearby road events, the controller lowers automation when map complexity rises, reducing processing load while keeping driving appropriate.

Wireless tags and multimodal shoe feedback guide pedestrians and warn of collision risks where line-of-sight sensing is limited.