Instruments For Road Network Navigation TRIZ Cases

Driver and vehicle coefficients enable fuel-use recalculation as fleet attributes change, improving prediction accuracy and cost control.

A dynamic lane-level graph uses connected vehicle data and uncertainty-aware weighting to guide lane changes without HD maps or precision GPS.

Aerial data layers aligned with real-time road perception improve vehicle localization accuracy without relying on high-definition maps.

Adaptive playback timing matches travel duration while linked group navigation shares environmental data with stronger privacy protection.

Adjusted playback rates match media length to predicted travel time, helping complete listening during navigation while protecting shared user data.

LiDAR or RADAR feed-forward traffic wave sensing helps vehicles adjust acceleration to keep distance and pace in stop-and-go traffic.

Multiple motion cues identify true straight-line travel so vehicle sensors can be calibrated in motion without losing alignment accuracy.

Captured in-car audio is matched to reference content so radio ads can trigger synchronized visual content such as QR codes.

Trajectory costs separate time-based state violations from fixed transition penalties, enabling rule-aware vehicle path optimization.

Image-based visual foundation models generate road layouts with local traffic rules, improving map accuracy for automated driving.

Geotemporal zones of intent help navigation identify emerging trip needs and add efficient POI waypoints with less delay and energy use.

Projects vehicle state data onto road or vehicle surfaces so speed, direction, and motion cues stay relevant without overly complex display hardware.

Road and object sensing flags HD map errors, separates temporary from permanent road changes, and adjusts autonomous driving control.

A multi-corridor route model uses perception data to place lane changes dynamically, improving trajectory feasibility and reducing route failures.

When a planned path is not permitted, alternative vehicle trajectories are generated and checked to avoid unnecessary stops and cut processing load.

Visualized HD map metadata inside a 3D driving simulation helps developers trace links, nodes, and signals to find errors faster.

Detects a connected trailer and guides EV drivers to charging stations with enough space, avoiding trailer decoupling and range-planning issues.

When turn guidance falls outside the HUD view, a shift mechanism moves it into the visible area so drivers can follow directions without looking away.

When a vehicle stops away from its stored parking spot, external environment recognition helps generate an accurate exit route.

Real-time reference points and a derived boundary line let vehicles cross four-way intersections safely without pre-mapped intersection data.

A virtual vehicle simulates tight turns, low bridges, traffic, and weather to reroute autonomous vehicles around infeasible maneuvers.

Sensor-based vehicle positioning aligns a chosen door with a detected passenger for easier access to assigned seats and luggage.

External status display alerts surrounding vehicles when autonomous driving starts, helping safer manual takeover during accidents or congestion.

Routes EVs towing trailers to charging stations that fit vehicle-trailer dimensions, avoiding decoupling and range shortfalls.

Indirect sensor evidence is used to infer occluded traffic signals and traffic flow, helping autonomous vehicles make safer driving decisions.

Fusing IMU, lane-line, and feature-point data improves vehicle pose stability and lateral accuracy in weak-texture, changing-light roads.

Historical-data simulations and confidence scoring help autonomous trucks choose routes that adapt to traffic, weather, and road closures.

Precomputed charging distance and lane selection help vehicles reach target battery levels with fewer charging sessions and less lane congestion.

Using V2X data from multiple forward vehicles, this case shows how average path control improves curve stability and safer platooning.

Projected road-surface light marks the autonomous vehicle stop point during parking lot exit, improving stop position recognition.

Candidate basis paths ranked by drivability help autonomous vehicles handle lane changes, merges, and obstacles with lower compute load.

Sensor-driven AI detects degrading vehicle attributes during driving and recommends timely actions to improve safety, longevity, and power use.

Sensor-based parking space volume estimation sets a safe engine run time during parking, reducing exhaust risk without cutting maneuver time.

Routes are calculated to avoid manual-driving switch points, improving dispatch completion reliability and reducing operator monitoring load.

By comparing vehicle positions on route and lane maps, this case enables fast rematching after branch-point deviations to keep autonomous driving continuous.

Adapts transmitted vehicle perception data to link quality so remote operators can modify trajectories while preserving safety and fleet use.

Using lane lines, host path, and nearby vehicle data, this case detects adjacent-lane congestion in real time without central infrastructure.

Maps only battery swap stations within remaining range, helping electric mobility users avoid unreachable stops and travel disruptions.

Route-based display control shows intersection animations only when conditions support clear guidance, reducing occupant confusion and processing load.