Road Vehicles Traffic Control TRIZ Cases

Predicted object trajectories and lane anchor points are combined to score future position-time pairs and steer self-driving cars away from collision risks.

Three interface modules coordinate driver, vehicle, and site operator handover to run autonomous utility vehicles securely in restricted areas.

Vehicle profiles guide a server to send only needed precise map layers, cutting transmission load while supporting reliable autonomous driving.

A pivoting boom separates the antenna from the camera to cut electromagnetic interference while keeping compact vehicle-side monitoring.

Motion, image, and position data are combined to predict boarding intent and open a vehicle door without passenger cooperation.

A server predicts where a vehicle will run low on energy and coordinates wireless transfer at an intermediate location using real-time traffic data.

When cloud reception stops, prioritized in-vehicle sub-functions keep driving support running within limited processing resources.

Tactile cues built from vehicle and surrounding data help remote operators grasp driving conditions faster during automated driving handovers.

An in-vehicle camera and conversion table map posted speed limits to the correct vehicle category, improving rule compliance and speed control.

HD map data and lane-conflict geometry infer stop lines where markings are missing, helping autonomous vehicles stop naturally and keep visibility.

Current scene data reconstructs past tracks to correct tracking errors and improve future trajectory prediction in automated driving.

Driver torque triggers a new lane reference path and lower alert threshold, enabling comfortable in-lane shifts without false warnings.

A movable mirror display relays phone text messages via Bluetooth so drivers can read them without looking away from the road.

Chance-constrained PAC-MPC links sensing actions with motion planning to cut uncertainty and avoid overly conservative autonomous driving.

Prioritized vehicle sensor data is merged into global tracklets to build lane-level traffic maps for more accurate navigation and control.

A conversion table translates posted speed limits to the actual vehicle category when road signs do not directly match the vehicle.

Limits pre-crest speed reduction on single-lane roads using downhill gradient and lane detection to cut queues, energy loss, and driver stress.

Distance-based display control keeps construction zone speed limits visible until the vehicle exits, avoiding incorrect database speed updates.

A mediator interface compares brake pedal and deceleration requests so ADK and vehicle platform can coordinate braking reliably under failures.

A two-stage sensor fusion scheme separates ASIL-B and QM processing, reuses first-stage data, and cuts controller load and latency.

Lateral path offsets spread vehicle loads across the route to reduce rutting and potholes while extending maintenance intervals.

Allocating AGVs to temporary parking points by missing-vehicle count reduces idle-busy imbalance and improves warehouse dispatch efficiency.

Forecasts projected path intersections and uses reach distance thresholds to slow or stop vehicles for collision avoidance, even underground.

Displays jam-related values and thresholds so drivers can anticipate automated driving mode changes and reduce confusion in traffic.

Time-parameterized trajectory optimization smooths parking maneuvers around obstacles while meeting steering, acceleration, and comfort limits.

Universal reference objects with shared identifiers and positions let different map vendors transfer location data accurately with less deflection error.

Separating road traffic alerts from other vehicle notifications avoids overlay confusion and preserves clear meaning on limited display space.

Adjacent-lane traffic evaluation lets longitudinal guidance adjust relative maximum speed to lower accident risk and improve driver trust.

Roadside units detect AV disengagement conditions, assign a control level, and send post-disengagement actions to reduce risky human takeover.

Aggregated vehicle anomaly data is mapped into autonomous driving danger zones, improving route safety while limiting privacy exposure.

By recognizing follow-up driver inputs, the system suggests target speed changes after speed-limit-based longitudinal control activation.

Standstill feedback switches from deceleration to stop-hold control, keeping autonomous vehicles stationary and avoiding unstable brake commands.