Instruments For Road Network Navigation TRIZ Cases

Projected ground charging along candidate routes helps an EV choose a path with enough noncontact power supply to reach the destination.

Fleet-sensed vehicle trajectories build and update map routes by comparing predicted and actual movement to keep autonomous driving paths accurate.

Loaded vehicles are barred from stopping at selected nodes, cutting uphill restart energy, wear, and traffic contention.

Polarized roadway imaging extracts prior vehicle paths to guide obstacle avoidance while preserving lane constraints and navigation rules.

Precomputed 3D parking cost grids let vehicles evaluate candidate maneuvers quickly, cutting processing time and compute load for real-time parking.

HD map harsh-environment data guides path search away from GNSS shadow areas and unrecognized road facilities for more reliable autonomous driving.

Remote operator takeover helps autonomous delivery vehicles handle sensor impairment, unauthorized passengers, and accessibility needs.

Routes are planned around locations where autonomous driving often switches to manual mode, improving completion reliability and reducing monitoring load.

Crowdsourced outlet registration and real-time status feedback help users find compatible, available charging points without manual searching.

When an autonomous vehicle failure occurs, prompted passenger feedback is linked to time, location, and event data for sharper analysis.

Road-segment elevation coefficients improve EV range estimates on hilly routes by modeling uphill and downhill energy use more accurately.

Map icons vary by predicted state-of-charge recovery at nearby charging facilities, helping drivers compare charging value at a glance.

Multiple camera captures before and after parking let the ECU compare current and stored lot data for more accurate autonomous parking.

A guide mark that mirrors the vehicle icon keeps intersection map zoom transitions visually continuous and improves turn guidance.

Dynamic pop-up timing uses entry-failure probability and traffic data to improve junction recognition and avoid missed exits.

Aligned candidate slices from adjacent lane connectors let carriageways be chopped into consistent links for more accurate mapping and repair.

Real-time bin fill and vehicle status data enable adaptive refuse collection routes that improve pickup efficiency and reduce operating cost.

Sensor-based frustration detection engages selected driving automation under difficult road, weather, or traffic conditions to reduce risky behavior.

Dynamic vehicle routing and automated queuing cut refuse transfer station wait times while predicting obstacles to improve safety.

Agent-based EV routing coordinates multi-service stops, battery swapping, and V2G energy use to cut computation time and operating cost.

Real-time sensor-based cognitive load estimation helps adjust secondary tasks, navigation, and audio to reduce driver distraction.

Digital map assignments link traffic signals to travel directions, improving distant signal detection for smoother automated braking and starting.

Combines bus data, vehicle dynamics, and 3D road maps to detect subtle tire, shock absorber, and chassis faults more accurately.

Infrastructure-guided valet parking moves vehicles from drop-off to open spaces and back to pickup areas, cutting search time and collision risk.

When SLAM detects a suspected obstacle, attention sensing decides whether to request driver input or autonomously route around it.

Transforms standard navigation routes into mapped lane segments with lane preferences, enabling autonomous driving on roads without detailed AV data.

Route-segment fuel evaluation uses vehicle specifications and speed profiles to generate tracks that cut energy use on specific road portions.

Reinforcement learning routes decoupled electric tractors and trailer swaps to avoid waiting for full charging and cut delivery delays.

Risk profiles and prior event patterns are used to create and deploy new fleet vehicle event definitions for more accurate detection.

Updates the vehicle image map only when movement or speed thresholds are met, cutting multi-camera processing load while preserving driver awareness.

An adjustable allowable limit line lets a work vehicle route closer to field boundaries while adapting to changing ground conditions.

An adjustable allowable limit line lets a work vehicle route closer to field boundaries while preserving a safe travel margin.

Fleet learning from real-road EV data updates energy consumption models to improve control factors and distance-to-empty accuracy.

A trusted planner checks part of an untrusted trajectory against safety constraints, cutting costly real-world validation time.

Navigation landmarks and emotion-tagged sound sources let an EV vary driving sound by route, replacing fixed manufacturer audio.

Route-segment data from preceding vehicles improves energy-use prediction and generates more accurate low-consumption tracks across vehicle types.

A trained model converts state data from multiple vehicles into road roughness and IRI estimates, enabling abnormal-surface mapping and route planning.

Route steering uses solar-vector alignment to avoid array shading, trading direct travel for higher photovoltaic energy generation.

A cloud planner selects fleet-specific motion planning systems and token-based access to generate compliant vehicle routes in real time.

GPS-defined halt zones let an ISA system decelerate trucks automatically and work across different cruise control modules to avoid low-clearance collisions.

Constrained quintic paths and cost-based selection help autonomous vehicles find complete routes quickly in cluttered, dynamic environments.

Pre-matched AV and pick-up/drop-off zone data helps autonomous vehicles route to usable curb locations faster and avoid time-consuming PDZ searches.

Multiple trajectory predictors are compared per object so the vehicle can switch to the most accurate model as conditions change.

Multiple vehicle trajectories are clustered into reference driving paths, improving HD map accuracy and automated guidance where lane markings are unclear.

Route-specific geometry, traffic, weather, and vehicle data are combined to generate an energy-efficient track with real-time driver feedback.

Map-based lane positioning and preceding-vehicle trajectory detection trigger timely takeover requests when sensor-based lane recognition degrades.

Remote sensor point clouds fill occluded zones in overlapping views, producing cleaner occupancy maps for autonomous path planning.

Context-aware speed warnings use map, camera, and driving behavior data to suggest deactivation when detected limits are unreliable.

Adjusting mirror-panel optical distance and free-form optics helps a compact HUD maintain virtual image visibility while correcting distortion.

AR overlays combine vehicle position signals and driver field-of-view data to locate emergency vehicles in low visibility with less distraction.

Layered map icons combine static location data with dynamic visual metrics, reducing per-icon clicks while keeping real-time updates visible.

Automatic emergency detection uploads media to the cloud, maps location data, and alerts supportive entities without manual setup.

When a vehicle nears its destination, the system prompts early route guidance termination to cut unnecessary interaction and keep driver focus on traffic.

Path segments are scored by wireless performance so routes can preserve driving assistance features and avoid unsafe assistance transitions.

A sweep-line path search structure finds Manhattan shortest routes between buildings while minimizing turns, execution time, and memory use.

Playback is shortened to finish before an autonomous driving ODD boundary, avoiding route changes or slower driving just to complete media.

Graph-based modeling makes geographic region optimization more tractable while preserving contiguity and balancing constrained resources.

Multiple vehicle accident notifications are counted against a threshold to identify pile-ups and warn approaching vehicles before they arrive.

Richly annotated route candidates let a language model interpret complex preferences and rank navigation options beyond time or distance.

Terminal-side speed-constant prediction fills gaps between server updates, improving nearby-vehicle location accuracy without proportionally increasing server load.

Preplanned walking sections before boarding or after disembarking add exercise opportunities while preserving convenient, efficient vehicle travel.

Stored landmark data helps calculate moving-object position without GNSS, reducing wheel-idling errors and processing delay.

An RL-GNN pools travel requests across service vehicles and adapts decentralized routes to demand spikes, delays, and supply changes.

Rather than building a separate database, the navigation service queries social media profiles for route-based points of interest and displays them on a map.

GPS multipath from high-rise buildings can misplace vehicles; grid scoring and convergence correct the position using sensing data.

Text-based POI selection replaces map rendering to cut data and power use while simplifying pickup requests in low-bandwidth settings.

Manual route creation causes errors and resource waste; spatial feature extraction and clustering identify viable network test routes.

User-selected start points, endpoints, and POI categories generate multiple cycling routes with estimated times, reducing manual planning effort.

Path delays can misalign vehicle radar and external-camera signals; image estimation and difference checks correct timing for precise motion estimation.

Dynamic marker sizing follows map scale to preserve individual marker visibility while reducing clutter and revealing trends at smaller scales.

Integrates route calculation, estimated arrival time, and vehicle transport booking to replace manual journey research and reservations.

Location detection triggers a tourist-road interface that overlays attraction data on navigation maps, avoiding interface switching.

Speech and photograph search help vehicle users identify locations and set navigation waypoints without recalling exact addresses.

The RMDA platform combines GPS, routes, and driver data to reduce information-gathering time and streamline daily coaching.

This display embeds reservable mobility options within tourist routes, helping users check availability and book travel in context.

LIDAR point clouds and convergence metrics identify stale map regions, enabling targeted updates for reliable vehicle localization.

A server proactively updates navigation options from location, plans, time, and local context as user situations change.

Trajectory data from nearby vehicles forms a reduced graph for lane-level maps that reflect temporary road disruptions in real time.

A reference entity shares standardized map-matching results to reduce computation and improve localization across different map providers.