Instruments For Road Network Navigation TRIZ Cases

Real-time sensor perception turns lightweight SD maps into onboard HD maps and lane-level trajectories, reducing offline updates and human validation.

An accessory-mounted control unit uses chassis-specific data to synchronize vehicle driving and work equipment for autonomous load handling.

Activity-based controls simplify map and media playback during driving, cutting redundant inputs, attention load, and power use.

Monitoring each vehicle subsystem lets the control unit replan route and trajectory so automated driving can continue safely after degraded performance.

GPS traces, speed, and parcel data are combined to identify waste collection stops and container locations without added onboard sensors.

Curated map and parcel features are filtered by distance to infer building access points for more precise autonomous vehicle pickup and drop-off.

Top-view matching between external sensor data and onboard camera images localizes a vehicle accurately without time-intensive HD map generation.

By sequencing heavier items earlier, this route planning approach cuts vehicle energy use, extends driving range, and helps reduce emissions.

When the main brake fails, the control unit cuts drive power, activates backup braking, and reroutes the vehicle to continue travel safely.

Presents acceleration and deceleration cues through vehicle HMI feedback so autonomous driving occupants can anticipate motion and feel less discomfort.

Probe-based crossing travel tracks generate travelable range data for automated driving through lane-crossing sections without costly sensor vehicles.

Real-time 3D terrain simulation updates route go/no-go guidance and vehicle settings to avoid sinkage and immobilization.

When a vehicle enters a roadside rest area, the system shows a route to the charging facility and suppresses unnecessary guidance.

By limiting road marking comparison to the pre-curve range, this case reduces misrecognition and stabilizes trajectory generation.

Camera-based road shape recognition identifies entrance and exit sections to generate intersection routes without high-precision maps.

Driving-pattern analysis predicts vehicle destinations and triggers arrival-time-based actions to improve efficiency and user experience.

A split local and general route planner cuts onboard computation and speeds autonomous vehicle response to changing road conditions.

Sensor-built self-generated maps improve vehicle pose estimation on lane-level maps in low-GNSS areas without costly receivers.

A regression neural network predicts ego-lane centerlines from camera images, cutting compute load and handling roads without lane markings.

AI detects a visually impaired rider near a rideshare vehicle and uses audio cues to guide boarding, seating, and obstacle avoidance.

Preloaded wide-area map ranges keep control map construction continuous during wireless dropouts, with local high-precision fallback for automated driving.

Power is allocated across vehicles by available supply capacity to avoid mid-operation depletion, reduce replacements, and keep facility output stable.

Separate map and road condition display areas adapt to ADAS operation and driver state to improve control understanding with lower display load.

A user-triggered disconnect isolates a compromised vehicle wireless network while local data supports navigation to a safe location.

Bounding-box, velocity filtering, and radar-GPS fusion help identify and track a lead vehicle to maintain convoy gap under ambiguous reflections.

Posture-based weighting of distance-sensor feature points improves mobile object localization and data association in biased environments.

Multi-point navigation cues on the windshield and road improve route clarity and driver guidance across changing driving conditions.

Dynamic parking assignment uses event-duration prediction and space availability to relocate autonomous transports, easing congestion and improving access.

Swarm GNSS positions are scored by standard deviation to correct inconsistent receiver accuracy and improve following vehicle control.

Road-segment hazard scoring combines driver, traffic, and weather data to reroute trips and trigger route-specific insurance bids.

Multiple road samples and adaptive speed models calculate curve-specific recommended speed to improve control accuracy and prevent overspeed.

Sensor-based detection confirms mapped charging stations before display and navigation, reducing map-to-reality mismatches in automated vehicles.

Guidance changes between battery swap and charging sites based on equipment, availability, and crowding to cut wait time and avoid unsuitable stops.

When HD map localization becomes unreliable, dual-mode planning switches to a map-less surroundings model to keep automated vehicles operating safely.

Yaw and roll control keeps windshield HUD navigation arrows visible and aligned despite map or sensor errors, reducing misregistration.

By comparing detected lane markings with map lanes, this case sets a corrected path center when one side is unreliable.

An ECU ranks vehicle loads and cuts lower-priority components at low charge, preserving power to reach a charging station.

Crowdsourced vehicle trajectories are converted into route-based following distance profiles to enrich maps for proactive spacing and safer automated driving.

Traffic-aware pick-up recommendations help autonomous vehicles reduce boarding delays and improve arrival timing without manual parking search.

Routes vehicles with detachable energy storage to charging or replacement sites based on availability, performance, and compatibility to avoid delays.

Sensor fusion and AR cues help drivers locate obscured hazards, road signs, and emergency vehicles with less distraction.

Complex routes are split into segments and matched with reusable scenarios to generate safety rules that satisfy both driving safety and route goals.

Incremental map change data lets vehicles update HD map features faster than full map redistribution, cutting latency and transmission overhead.

Detects restricted objects in an autonomous vehicle and switches to compliant routes that avoid prohibited areas while notifying passengers or authorities.

Type-aware feature point matching links camera images to map objects more accurately, reducing false matches in vehicle positioning.

Route segmentation at predicted speed-change points estimates needed charge and finds a charger that avoids power shortfalls and long stops.

Potential energy grid mapping and parallel track costs enable smooth in-lane obstacle avoidance without lane changes or control oscillation.

Multiple communication channels deliver autonomous driving visibility data and lane-based routes from HD maps with better reliability and navigation precision.

Route generation combines unmanned pickup, self-driving, and manual segments to avoid detours, follow sidewalk rules, and match user preferences.

Temperature data along candidate routes is used to predict arrival battery capacity and avoid low-temperature battery deterioration during EV guidance.

Onboard mapping, localization, and task execution let a material-transport vehicle navigate industrial facilities without centralized infrastructure.

Combining bank scan matching with landmark sensing keeps work machines accurately positioned when terrain shapes change and map matching degrades.

AI-driven routing assigns concurrent tasks by vehicle availability and sensor data to improve utilization, route order, and revenue.

Weighted route selection balances travel time and energy use with real-time road, traffic, terrain, and vehicle data.

A single planar beam and detector array replace many fixed LiDAR beams, cutting power, processing load, and system complexity.

Calculates joining cost from vehicle locations and routes to choose a transfer vehicle and meeting point with lower rider burden.

Infrastructure sensing and stored vehicle dimensions let a server judge obstacle passability and send detour routes to affected vehicles.

Pre-assessed pickup and destination suggestions help autonomous vehicles avoid unsafe or inaccessible stops while preserving service coverage.

Selective location sharing lets a service assignment system choose AV pickup stops that shorten walking distance while protecting user privacy.

Road-surface magnetic distributions help vehicles match sensor readings to map data for more accurate positioning on high-precision 3D maps.

A shared goal representation helps robot navigation generalize across tasks, reducing unforeseen model errors in dynamic environments.

Sensor-driven occupancy grids let a summoned vehicle plan and update safe paths through crowded parking lots without straight-line remote control.

A hierarchical big cell grid map cuts memory load while preserving fast access to feature points for accurate real-time indoor robot pose estimation.

Checks whether a remote rental car return route stays within communication coverage and suggests alternative drop-off points when it does not.

Manual training captures vehicle sensor data to build parking-route maps, enabling valet parking assistance in unmapped areas.

Mobility-based static scores help autonomous vehicles filter dynamic objects from maps, improving localization and route planning.

AI routing schedules concurrent vehicle tasks, updates routes from sensor data, and improves revenue-focused fleet utilization.

A single planar beam and detector array replace many fixed LiDAR beams, reducing power, cost, and processing load while preserving data quality.

Balances travel time and energy use by weighting road segments with real-time vehicle, traffic, terrain, and weather data.

High-accuracy UAV surveys feed weighted terrain maps that speed reliable ground vehicle routing across unfamiliar or evolving terrain.

LiDAR layers and image resolution analysis correct UAV altitude in low-visibility flight, improving obstacle avoidance and route safety.

Video-based behavioral models link road context to likely bicycle or skateboard maneuvers, improving path prediction for traffic navigation.

Conflict-point detection and congestion-area clustering help sorting vehicles reroute around blocked zones and maintain distribution efficiency.

Always encoding the merge flag and pre-adding zero-motion candidates makes video bitstream parsing more independent and error resilient.

Subscriber profiles and route data drive network-slice vehicle settings for comfort, media, seating, and detour-aware navigation.

Path-guided mobile waste boxes use cameras, sonar, radar, and LiDAR to collect waste without user calls, waiting, or visible disruption.

In-situ sensing and prior field-operation maps predict crop conditions ahead of harvesting, enabling automated setting changes for steadier yield.

Object detection from LiDAR and HD maps narrows search space for real-time vehicle localization when satellite positioning is weak.

Token-based controller handover lets autonomous inventory units cross control territories securely while reducing latency in multi-site transport.

Localized footprint, scan consistency, and alignment scores detect and correct diverged robot maps before navigation errors and collisions occur.

Historical spatiotemporal event models guide autonomous vehicle routes to avoid intervention-prone scenarios while balancing travel time and risk.

Image recognition and onboard sensing let a shovel estimate each transporter vehicle's remaining time without fixed positioning infrastructure.

Fused 3D imaging, visual odometry, and GNSS reduce drift and keep vehicles aligned through row ends when GPS is obstructed.

Fleet trip data is used to build canonical routes that improve autonomous vehicle navigation efficiency while reducing interventions and hazards.

A fixed merge flag plus zero-motion fallback candidates reduces HEVC bitstream parsing overhead and improves decoding error resilience.

Shared 3D worksite mapping guides haul trucks to exact dump points, reducing grading rework and improving discharge accuracy.

Temporal sensor fusion and conflict arbitration improve occupancy grid accuracy for obstacle detection and drivable space mapping.

Real-time traffic prediction and route alternatives help buildings cut peak power and energy use while maintaining resilient passenger access.

Per-trip route pricing adjusts vehicle insurance by location, route, weather, road conditions, and driver behavior to lower costs for infrequent drivers.

Altitude-aware route planning widens lower headlands and adjusts turning radius to keep autonomous tractors from sliding off inclined fields.

Real-time coverage data lets an autonomous farm vehicle adapt path plans to changing implement widths and choose the lowest-cost route.

Multiple agents share LiDAR-based frames and maintain independent map graphs to improve 3D mapping accuracy, coverage, and real-time scalability.

Combining less-frequent navigation maps with newer autonomous-driving map data reduces road-display incongruity and fills guidance gaps.

Altitude-aware headland widths and turning radii help autonomous tractors avoid slide-down and field departure on sloped turns.

Compares pose-based and LIDAR landmark predictions to correct autonomous vehicle localization when direct sensor data is limited.

Coordinated ground and aerial vehicles transfer items at planned handoff points to bypass obstacles, shorten delivery time, and improve routing.

Historical user data predicts pickup and destination options, pre-assigns vehicles, and cuts empty miles and pickup time.

Microwave sensing detects passive roadway lane markers in snow, fog, and glare, helping autonomous vehicles navigate accurately without constant map updates.

Segmented climb, cruise, and descent models use least-squares fitting on flight data to improve convergence and predict aircraft trajectory points.

Intermediate communication relays keep unmanned vehicles connected across long routes despite weather, distance, and bandwidth limits.