Instruments For Road Network Navigation TRIZ Cases

Real-time charger location and SoC checks flag unnecessary or excessive fleet charging to cut energy waste and downtime.

A lead AV detects construction zones, updates its route, and shares sensor data so following AVs can detour or reroute safely.

A lead-vehicle speed profile is used to generate route-specific convoy tracks that cut highway energy use across different vehicle types.

Replay real driving logs in simulation to detect progress and behavior gaps between human-driven and autonomous vehicles for safer software validation.

Resuming parking route training from a chosen stored position avoids full retraining while preserving route continuity and accuracy.

Candidate basis paths are generated to merge at selected lane points, improving autonomous lane changes while limiting processing load.

Sensor-driven predictive models detect vehicle status early and trigger speed or mode restrictions to prevent component failures during travel.

Left-right obstacle decisions made before smoothing keep unmanned vehicle paths consistent, feasible, and collision-aware.

Lane detection and scene-image fusion replace HD maps to show vehicle driving state with lower storage, computing, and network load.

Road surface snippets are scored for distinctiveness to build updateable landmarks that localize vehicles beyond GPS accuracy limits.

Buffered GNSS and gyro motion data identify flat ground at vehicle stop, enabling accurate sensor calibration without OEM data.

Pre-trip stall factor detection redirects unoccupied autonomous vehicles to alternate routes, improving trip completion when help is unavailable.

Uses driver-set minimum battery reserve and available range to flag charging needs and guide EV routes with less range anxiety.

Fleet energy data and confidence intervals improve EV route prediction under traffic and driver uncertainty, guiding mode changes or charging stops.

Driving conditions and navigational data are used to vary sports content detail, preserving relevance while limiting driver distraction.

Obstacle-aware trajectory combinations are pruned outside spatial or temporal corridors, cutting search time while preserving valid path choices.

Multi-plane AR on a vehicle HUD pairs turn symbols with road-grounded cues to expand limited field of view and improve maneuver guidance.

At intersections, collision-risk assessment and road topology guide exit-lane choice to improve vehicle safety and reduce calculation overhead.

Rule-based geometry extraction turns SD road maps into HD lane and traffic layouts faster and at lower compute cost for autonomous training.

By switching from HD maps to lower-precision road maps before coverage ends, the controller avoids incorrect speed-based acceleration or deceleration.

Map and sensor matching lets a vehicle switch smoothly from general road assistance to region-specific travel in designated areas.

A reference target and periodic correction keep parking guidance stable despite camera angle shifts and dividing line recognition errors.

When position accuracy drops, reliable voxel regions guide route correction and NDT matching to improve onboard vehicle localization.

Driver behavior, vehicle data, and trip constraints are combined to predict fuel use and present trip options that cut consumption and travel time.

Pre-filtered conveyance requests match representative preferences in real time, improving dispatch decisions, vehicle use, and earning potential.

By combining vehicle sensing with network data, the AR interface predicts hazards and shows separated guidance objects for safer driving.

Automatically plans charging stopovers and minimum battery charge levels so an EV reaches its destination with the required residual range.

Telematics data links weather, road conditions, and driving events to battery strain, enabling EV routes that protect battery health and guide assistance.

Dynamic mode switching adjusts sensor range and speed so self-driving vehicles can avoid collisions while limiting energy use.

Location indicators and section maps guide mobile pickers through variable store layouts, cutting order gathering time and route inefficiency.

Fused map, sensor, and dynamic object data improve lane-keeping visibility and support adaptive lane control in changing road conditions.

Aligned light regions in a vehicle cabin vary color and intensity to convey navigation, audio, and vehicle status without obstructing driving.

Map-based lane highlighting shows which lanes support autonomous driving, the current lane, and remaining auto-drive distance for safer transitions.

Scaling HUD objects to vehicle orientation corrects low-viewpoint distortion, improving recognition and realistic driver display.

Vehicle sensor data maps tire abrasion pollution by location, filling gaps left by fixed air stations and enabling real-time cabin air and route control.

Roads are split into lane groups with virtual points to deliver accurate lane-level guidance on complex roads while limiting processing burden.

Virtual windshield guidance points drivers toward turns and lane changes while filtering relay point data to keep route cues accurate.

By merging multiple observed vehicle paths into one representative trajectory, autonomous driving can adapt to lane shifts and roadwork with better map accuracy.

Multiple positioning engines are fused, then UI corrections refine lane-level vehicle position estimates for safer autonomous navigation.

Camera pointing and sensor data identify nearby vehicles for spontaneous digital hailing, adding verified driver info and safer ride requests.

Past driving habits and future trip data are combined to predict fuel consumption more accurately for trip planning and fuel management.

Sector-based validation checks sensed objects against map data to improve autonomous vehicle positioning on slopes and around 3D objects.

A dual-planner approach filters trajectories for safety first, then preserves driving quality with state-aware planning in real time.

By merging network and external sensor data, this case improves routing reliability for real-time item location and autonomous pickup.

Real-time vehicle and map data limit or correct AR signage during turns, roundabouts, and route changes to protect driver focus.

3D vehicle path projections separate true collision threats from nearby objects, reducing unnecessary stops in narrow autonomous driving spaces.

Prevents map and video combinations that overload in-vehicle displays by blocking unsafe screen layouts before rendering issues occur.

When brake abnormality is detected, satellite-based guidance selects a safe evacuation site and route using road friction and congestion data.

Distributed computing nodes pre-position and exchange vehicle data along predicted routes to cut transmission delays and improve route updates.

Real-time battery power and speed data feed an ML model to update EV charging stops, improving route accuracy and reducing unnecessary charging time.

Minimizing distortion between measured sensor constellations and CAD references enables autonomous chassis frame estimation and sensor calibration.

Timed auxiliary driving prompts combine navigation and sensor data to deliver lane-change or deceleration guidance with better precision and fewer late alerts.

Predicting route-specific mobile network QoS helps connected vehicles avoid poor coverage and maintain reliable service across operators.

Predicted traffic stacks at intersections trigger roadgraph cost changes that encourage lane changes and help autonomous vehicles keep moving.

A removable auxiliary battery handles peak vehicle energy events, then shifts load back to the core battery to limit degradation and overheating.

Neural-network traffic prediction anticipates hidden-object hazards in control zones and reroutes vehicles before unsafe behavior causes conflicts.

Fused LIDAR, camera, and GNSS terrain maps let agricultural vehicles anticipate slopes and obstacles for smoother steering and implement control.

Geohash-based merging aligns crowd-sourced and operator data to remove duplicate charge station locations and improve search accuracy.

Credibility scoring filters and promotes shared map updates, enabling reliable cross-platform truth maintenance without manual intervention.

Combining movement restrictions with observed driving behavior adjusts path cost volumes to score safer, more human-like vehicle trajectories.

Temperature-based gyro sensitivity correction and GPS feedback keep vehicle orientation estimates stable and reduce display image deviation.

By correcting reference energy at route points with high actual-use correlation, this case improves vehicle energy prediction accuracy.

Projects only available parking spots onto real space using sensor recognition, helping drivers park with less distraction and no added signage.

Predictive road-risk scoring combines vehicle, weather, and accident data to reroute autonomous vehicles and trigger timely alerts.

Trip history analysis identifies repeated pickup and destination patterns, then suggests and stores autonomous ride routines for faster booking.

Predicted vehicle motion aligns delayed environment sensor signals with the correct time point, improving detection and calibration accuracy.

A shared registration point lets multiple vehicles compare distance to home, helping drivers plan parking order and avoid vehicle switching.

Curve segments are shifted and rotated within tag-based tolerances to detect map feature changes from vehicle sensor coordinates.

A trusted planner cross-checks trajectories from a new autonomous driving planner, reducing costly road validation while maintaining safety.

A vehicle context engine enriches voice prompts with sensor, location, weather, and preference data to deliver more accurate responses with lower energy use.

Camera image analysis predicts wheel rotation and flags slip from sensor mismatch, helping autonomous vehicles navigate in real time.

Past running locations and vehicle data are screened to generate travel routes that better match user preferences and driving conditions.

Directly comparing lane and trajectory projections in camera image space improves lateral guidance accuracy while avoiding costly global mapping.

Route tracking triggers speed limiters, braking, or autonomous functions to keep fleet vehicles on recommended routes and cut operating costs.

Residual and variance data feed a machine learned model to flag vehicle localization errors early and support alerts or safe-stop actions.

Marker pair matching with coarse and fine updates cuts ICP and RANSAC burden for fast, accurate vehicle positioning in fixed parking.

Curve speed limits are derived from route radius and driver-specific lateral acceleration data to improve warning accuracy and driving flow.

Animated AR head-up display elements trace upcoming bends in the driver’s view, improving route awareness without dashboard distraction.

Routes users on foot to eligible pickup points so autonomous vehicles avoid stopping restrictions and shorten total trip time.

Geolocation matching flags assets inside predicted disaster zones, identifies safe areas, and alerts owners to relocate before damage.

Real-time charger checks and interest-based waypoint selection cut EV rerouting and time lost searching for available charging stations.

A hierarchical Dempster-Shafer classification tree tracks multiple object states with confidence-based updates for more accurate AV perception.

Adaptive localization shortens the estimation cycle in rain or fog to sustain landmark-based vehicle positioning accuracy and reliability.

Trip-route analysis identifies where EV travel lacks charging access, guiding collective charger placement and network optimization.

Vehicle and weather data are fused into localized road risk maps, helping vehicles detect slippery regions and adjust control in time.

Layered HD map conversion into an SD-compatible format cuts storage burden and speeds vehicle map processing while preserving precise navigation.

Predicts the next driving type and target battery level at arrival, adding charging stops only when needed for later trips.

Real-time tracking and feedback limit PMV speed and performance by rider experience to reduce unsafe operation in transportation networks.

Route geometry, elevation, and traffic data are used to generate energy-efficient tracks that improve fuel estimation across different vehicle specifications.

Zone-specific roadgraphs let autonomous vehicles switch between street rules and free-drive areas for safer, more flexible mission execution.

Multiple barometric sensors on different vehicle surfaces detect motion and airflow direction, improving tracking when IMU drift or satellite loss occurs.

A movable sensor inside the vehicle mirror expands field of view and cuts visibility gaps while staying protected from external damage.

AR windshield alerts fuse sensor data to reveal obscured hazards, road signs, and emergency vehicles while reducing driver distraction.

Failure events trigger an in-vehicle feedback interface that links human input to time, location, and vehicle data for better evaluation.

A transfer point is chosen from both vehicles' positions and remaining energy so long-distance EV deliveries can finish without depletion.

Location-triggered battery depletion uses accessory loads and cell balancing to meet transport SoC limits without extra driving time.

Estimated passenger entry or exit time is matched to stop thresholds so autonomous vehicles can choose safer pickup points with less traffic disruption.

Wind-aware platoon control estimates drag and parasitic losses by route segment to choose vehicle groups and travel plans with lower energy use.

A server predicts event duration to reassign autonomous transports to nearer spaces as availability changes, easing congestion and improving parking use.

Projects map objects into vehicle images and uses coordinate channels to improve depth estimation, object detection, and trajectory prediction.

Map open-space data and guaranteed position ranges help validate vehicle road class when satellite positioning uncertainty is too large.

A control unit treats unrecorded map content as absent data, enabling one information-acquisition method across different autonomous driving maps.

Bulk-conductivity modeling with ground effects enables long-range through-wall 3D indoor position and orientation sensing from external nodes.

Acoustic and acceleration fingerprint maps improve vehicle localization and route comfort scoring when SLAM positioning is imprecise.

Ideal travel positions and driver rewards guide vehicles to gather requested road data more efficiently during normal travel.

Bias transformation corrects manually observed object corners across sensor images, improving labeling consistency and map update accuracy.

A loader detects truck position and remotely repositions it to improve loading on uneven terrain while reducing material spillage.

Sensor feedback and preferred-site data let an autonomous vehicle switch to a safer, more accessible service location while en route.

Multiple vehicles split and optimize pose subgraphs in parallel to keep HD maps accurate, current, and practical for autonomous navigation.

Distance-based histograms link low-density sensor point clouds to dense map references, improving mobile body position estimation.

Pre-delivery user location checks let an autonomous vehicle reroute to the recipient's current position, avoiding redelivery and wait time.

Road segments are scored for autonomous driving suitability so routes can avoid fog, rain, sensor faults, and other high-risk conditions.

Presearched extra routes at a diverging point prevent rerouting delays and keep ECU-navigation cooperation continuous during autonomous driving.

Cloud-connected vehicles capture and deliver on-demand remote location imagery without fixed cameras, while blurring restricted areas for secure access.

Tracks listing views, trip starts, progress, and arrivals to measure ad-to-trip conversion and support trip-based pricing.

Real-time vehicle location and operator limits are used to assign on-demand deliveries with minimal route deviation and timely fulfillment.

A universal HD map server converts proprietary tiles and crowdsourced sensor data to expand autonomous vehicle map coverage with consistent QoS.

Separate speech and sound-effect guidance channels help drivers distinguish navigation from automated driving cues without overlapping audio.

Local and distributed analysis of mobile sensor data cuts bandwidth and computing load while generating accurate trip records and user activity insights.

Route segments are characterized by fuel and battery use so multimodal vehicles can choose paths that cut energy consumption and preserve charge.

Aggregated sensor, camera, and transaction data ranks routes to open parking spaces, cutting congestion, search time, and fuel use.

Lane-based grid shaping cuts unnecessary cells in curved-road ADAS, reducing memory use and computation while preserving relevant occupancy data.

Area-based dispatch first checks nearby manned vehicles, then assigns an autonomous vehicle to cut search time and improve travel efficiency.

Vehicles compare onboard sensor data with stored maps in near real time and broadcast local road-change alerts to nearby traffic.

Road geometry geofencing and kinematic filters correct GNSS multipath and dead-reckoning drift to keep vehicle positioning at road level.

Dynamic item files, trigger detection, and onboard sensors let vehicles coordinate pickups in real time while improving fuel use and connectivity.

Real-time sensor and network data guide VTOL speed, propulsor use, and routing changes to cut urban noise impact.

Routes are selected from geo-tagged signal, call-drop, and service data so drivers can balance travel efficiency with reliable wireless coverage.

Machine learning scores telematics data quality from GPS and trip metrics, flagging weak mobile devices before driver scoring.

Driving images and confidence calibration improve lane prediction accuracy beyond GPS, supporting stable guidance and autonomous driving.

Non-linear analysis of interaction and sensor data derives effort metrics to adapt therapeutic UI elements and improve user engagement.

Routes are generated from vehicle, traffic, driver, and charging-stop data to balance electric efficiency, range, and arrival time.

Fixed recurring ride values are generated from location and time windows, cutting repeated queries, UI steps, and matching inefficiency.

Real-time sensory fusion builds a live map and adaptive paths with haptic and audio cues for safer, more accurate navigation.

Predefined detection zones and longest-pattern matching identify roadway trips without fixed entry or exit points, improving billing accuracy.

An AI engine groups routes by location and time to reassign service across vehicles, reducing redundancy, fuel use, and maintenance.