Arrangements For Variable Traffic Instructions TRIZ Cases

A universal HD map layer translates proprietary tiles and crowdsourced vehicle data to expand coverage while preserving map service quality.

Predicting traffic light timing and compensating for control delay lets remotely driven vehicles react on time and keep traffic moving.

Predicts conflicting arrivals at remote-operation locations and issues control information to prevent congestion and remote monitoring overload.

Merging adjacent underground work zones into a fusion zone lets one autonomous vehicle pass without stopping others, improving access and continuity.

Pre-checking communication quality by terminal type helps remote driving start only under stable links, reducing interruption risk.

Dual communication modes switch manned vehicle location updates from coarse to fine granularity near autonomous mine vehicles, reducing unnecessary stops.

Detailed V2X message elements for yaw, roll, pitch, brake lights, and turn signals improve vehicle motion prediction and maneuver planning.

Machine learning prioritizes risky vehicles and monitoring targets, cutting remote agent workload while preserving coverage.

Adaptive V2X message class selection uses channel busy ratio and recipient state to ease congestion while preserving cooperative driving reliability.

Maximum wait time control sends self-driving vehicles back to parking when pick-up occupancy runs long, easing congestion and turnover.

Continuous diagnostics and multi-core redundancy detect roadside sensor link failures early, discard bad data, and avoid shutdowns during maintenance.

Detection data from passing vehicles lets a server estimate magnetic marker and sensor condition, reducing routine road inspection costs.

Weighted fitting gives steering-range points higher priority, improving route accuracy and curvature smoothness for more comfortable vehicle control.

A server-mediated end request lets remote driving continue past arrival only when needed, improving service closure and charging accuracy.

Nearby vehicles share supplemental sensor data to fill occluded scan regions, improving autonomous navigation confidence and collision avoidance.

Pre-verified operator skill and authentication enable smooth switching between driver-operated and remote driving while restricting unsafe functions.

Maximum wait time control reroutes self-driving vehicles from pick-up places back to parking spaces to prevent departure congestion.

A universal HD map structure lets autonomous vehicles use and contribute map tiles across proprietary providers while preserving service quality.

A mobile display reaches flexible bus stops before arrival to mark the pickup point, guide riders, and deter other vehicles from blocking it.

Sensors and onboard communications let drones assess crash scenes, redirect vehicles, and adjust signals to cut disruption time.

Dynamic queueing separates at-risk pick jobs from standard work, balancing due-date adherence with fulfillment throughput and resource use.

Physics-based genetic path planning cuts manual mission planning time while improving trajectory accuracy across air, sea, and land assets.

Route-based broadcast planning times road, news, and ad content around automated-to-manual handover to keep drivers informed without overload.

A three-tier server architecture keeps vehicles continuously connected during area handover, reducing control-server load and latency.

BLE advertising packets add location and purpose data from roadside signals, helping autonomous vehicles navigate crosswalks and work zones.

A remotely updated digital key lets users access a vehicle pool, then locks control to the selected car to balance convenience and security.

Virtual path areas and passing sequence control coordinate mobile body timing to prevent collisions and deadlocks at contested route sections.