Instruments For Road Network Navigation TRIZ Cases

A remote computer ranks donor vehicles, guides both vehicles to a charging point, and supports unattended EV-to-EV charging.

Constraint-bound topological planning prunes redundant maneuver sets so autonomous vehicles can find feasible trajectories in dense traffic.

Pattern-based surface region filtering helps vehicle sensors separate stationary road users from landmarks for safer trajectory planning.

When onboard sensors leave blind spots, external vehicle or infrastructure data is merged to complete maps and support safer driving.

Virtual lane and stop-line management guides automated vehicles around obstacles and varying road rules for safer, more efficient travel.

Route, traffic, and battery data are combined to predict EV remaining charge and guide charging station selection for stable transportation.

Independent prediction and short-term motion analysis help autonomous units react faster to moving hazards while limiting computing load.

Critical locations are matched to software ODDs and vehicle routes so shadow mode testing targets challenging scenarios without continuous compute load.

Human driving data labels and prunes useful graph edges, cutting path-planning complexity while preserving high-quality vehicle routes.

Dynamic parking reassignment uses event timing and space availability to move transports closer as spaces open, easing congestion.

Decentralized V2X maneuver assessment lets autonomous vehicles negotiate conflicts locally, improving reaction time and safety without central control.

Historical charging success records are aggregated into station evaluation values, helping users find more reliable EV charging locations.

Wheel and vehicle speed comparison detects slip in self-driving vehicles, enabling motor and braking corrections to maintain traction.

Section-based regulatory map data lets vehicles switch autonomous driving functions by location, improving legal compliance when sensors are uncertain.

Helmet-mounted AR guidance adapts to driver position and vehicle dynamics to improve track driving, safety, and driver focus.

Multiple target coordinates are used to detect both pitch and roll of a vehicle imaging device, improving object positioning accuracy.

Motion-parameter prediction and state vector correction reduce noise-driven road marking errors and improve stable input for driving automation.

Dynamic weighting lets an autonomous vehicle balance transport operation and deeper self-diagnosis, improving fault detection and availability.

Split evaluation units keep travel plan calculations running during circuit reconfiguration, enabling faster trajectory updates when obstacles appear.

Uses simplified long-horizon planning plus safety-margin constraints to set traffic-rule-compliant vehicle goals in real time.

Map data is split by region and travel mode so autonomous driving control gets only relevant lane and road information with lower transmission load.

Fused LiDAR points and image data automate object registration in vehicle maps, improving annotation accuracy and reducing manual effort.

Imaging and positioning data help an autonomous vehicle identify cyclist passing scenarios and plan safer, road-aware overtaking paths.

Actual node passage times are used to delay following vehicles only when needed, preventing parking lot merge collisions without changing passage order.

Automated travel planning uses vehicle status and destination data to choose owned or rental transport and trigger navigation or access steps.

Mid-air sensing between the steering wheel and windshield lets drivers interact with HUD images with less eye movement and no separate touchscreen.

Vehicle-specific route suggestions are preprocessed from external data and anonymized identifiers to improve navigation accuracy, privacy, and battery use.

Sensitive input explanations show why one motion plan is preferred, improving transparency and user trust in automated actions.

A battery-free Bluetooth beacon detects a carried bicycle so the car can recommend nearby bike trails, routes, and parking based on rider preferences.

Dynamic virtual lane and stop line updates help automated vehicles avoid obstacles and coordinate safer traffic flow.

Object-specific cost data helps autonomous vehicles choose whether to pass or follow nearby traffic while avoiding collisions and limiting planning load.

Groups lane markers and surrounding vehicles by common curvature to improve ego-vehicle path planning when markings are worn or occluded.

Computes ETA and route from a vehicle's degraded state after an accident, enabling safer continued driving and targeted map updates.

Road-segment learning lets on-board diagnostics avoid noisy or unstable engine conditions and schedule routines where IUMP completion is more likely.

A two-stage trajectory ranking approach filters unsafe paths early, improving autonomous vehicle navigation reliability without full-path analysis delays.

Uses driver behavior and actual route energy use to update range prediction and charging stops, helping EV drivers avoid running out of charge.

Gaze-tracked viewpoint switching and seat-based audio make remote users feel present inside a mobile object's cabin during real-time communication.

Confidence-scored path planning lets unmanned vehicles switch between autonomous and remote-assisted navigation in unfamiliar terrain.

Consistent GPS pings and low-speed stop detection map waste customers and container locations without added sensors or manual logging.

Matches cloud services to vehicle location and capabilities, enabling OEM-agnostic features with local buffering for weak network areas.

A moving speed display paired with an adjacent upper ACC display helps riders recognize automatic travel status earlier without a bulkier meter.

Oblique images, map data, and ground sensors locate obscured traffic signals more accurately for autonomous intersection navigation.

Using road boundary lines and arc-length coordinates, this case improves curved-road lane positioning and distance judgment for autonomous driving.

By matching captured in-car audio to reference ads, the display can show synchronized visuals, QR codes, and location-specific offers.

Multiple localization methods narrow initial position uncertainty before map matching with sensor data delivers reliable vehicle positioning.

Multiple cameras, HD maps, and sensor-quality weighting improve host-lane detection when GPS and single-camera inputs are unreliable.

When road markings are incomplete, this case uses GPS, lane geometry maps, and camera data to estimate sub-lane vehicle position accurately.

Similarity-based decoding of spectral route profile data cuts transmission load while enabling real-time vehicle control and stability improvement.

Expert-system EV routing compares battery limits, charging stops, traffic, and road conditions to choose practical routes with less range anxiety.

Marker angle and size are varied with target distance so a vehicle HUD preserves distance cues and improves relative speed awareness.

A localization system calculates metric position estimates using a non-linear loss function with a Kalman filter to determine vehicle location.

A multi-modal navigation system determines routes across transport modes using GPS data and outputs instructions to connected devices.

Builds a traffic prediction model from aggregated demand data to detect route overlaps, reducing congestion caused by outdated information.

Autonomous delivery vehicles scan urban routes to detect sensor deltas and trigger re-mapping actions.

Information processing apparatus acquires sponsor data and provides it to a ride sharing coordinator.

Portable devices read destination tags to guide users through buildings where satellite signals fail.

A navigation display magnifies selectable element surroundings at a larger scale than the map section for intuitive selection.

Predicts driver assistance functional quality via environmental models to prevent unexpected deactivations during driving.

A tracking device communicates with a building controller to automate system operations based on vehicle location data.

Dynamic mesh networks reduce latency and optimize energy efficiency for mobile nodes without centralized control.

Dynamic offset calculation corrects lateral errors in towed implements, resolving path deviation without manual operator intervention.

Structured navigation metadata transmission reduces bandwidth consumption while enabling real-time 3D image generation in vehicle optical displays.

A trajectory planning method segments driving areas to switch between on-lane and open-space modes.

A vehicle telematics unit transmits location data to a mobile device for direct user access.

A 3DOF trajectory linearization controller coordinates steering and speed to track spatial paths.

A navigation system gathers past location data to determine travel objectives and generates optimized routes using real-time traffic information.

A mapping application predicts future device destinations and presents dynamic notifications with route information.

A self-contained vehicular navigation system uses wheel rotation and magnetic sensors to determine vehicle speed and heading without continuous GPS signals.

A shape filter classifies GPS samples by trajectory geometry to separate accurate data from noise.

A vehicle control system detects surroundings features to assess planning map validity and determines driving functions based on that assessment.

Navigation platform separates probe data into distinct speed profiles for multi-modal road segments, resolving average speed inaccuracies at intersections.

A vehicle computing unit queries an external processor for component functionality data in a target region.

Distributed online learning synchronizes personal predictive models using decay factors to prevent overcorrection and maintain consistency across apparatuses.

A map display device allocates weather information to split screen areas using satellite radio reception.

Axle accelerometers detect road events via Z-axis spikes, enabling real-time data sharing that reduces fuel costs and maintenance overhead.

A tracking device divides service areas into regions to coarsely identify location and determine accurate position only when predefined events occur.

A processing device generates visitor traffic lines by retrieving shortest routes from map information.

Vehicles transmit measured values between each other to validate data and reduce measurement errors in driver assistance systems.

A navigation system projects spline control points onto a slope plane to restore true road curvature.

Clusters vehicle event reports to reduce data volume while maintaining navigation accuracy.

Classification-based filtering extracts relevant images without requiring precise spot specification, resolving operation complexity trade-offs.

A vehicle warning unit directs airflow toward the driver to signal imminent danger without occupying visual or auditory channels.

A computer system determines electric vehicle travel distance and predicts charging station availability to recommend optimal routes.

A portable navigation device processor monitors ambient light levels to dynamically adjust display brightness and color schemes for optimal visibility.

Segmenting route maps into dynamic chunks resolves the contradiction between complete navigation information and limited device memory storage.

A management server selects candidate vehicles with sufficient power storage to facilitate peer-to-peer charging between electric vehicles.

A single navigation filter fuses multiple IMU and GPS sensor inputs to compute a unified position solution.

A display controller manages screen transitions between two displays by grouping screens based on function.

Abstraction of sensor data into features and context information reduces transmission volume from 400 to 60 bits per second.

A navigation system calculates initial route costs to guide drivers back efficiently after deviation.

Mobile computing device predicts destination using location history to generate relevant search results.

A prediction engine analyzes user-specific data to formulate predicted destinations and routes.

A travel support device assigns battery energy modes based on road loads.

A vehicle location system fuses GPS data with speed measurements using a Kalman filter to estimate real-time position.

A distributed navigation architecture segments platforms to combine inertial and absolute data sources.

A vehicle display unit shows the route to the nearest branch road and its direction during autonomous following mode.