Road Vehicles Traffic Control TRIZ Cases

Combining global and local self-learning models improves fleet arrival prediction by adapting to vehicle-specific and route conditions.

Formation-based maneuver planning uses tree expansion and cost-optimized trajectories to choose collision-free multi-vehicle actions in traffic.

Dependency-based vehicle grouping splits MUTEX-zone trajectory planning into parallel subproblems, reducing computation while preserving safe fleet coordination.

Direction-based exclusive areas block opposite-moving mobile bodies while allowing same-direction travel to suppress deadlocks and collisions.

When an area becomes non-operable, the system flags affected routes and sends route-specific cease-operation instructions to vehicles.

A server dispatches camera-equipped autonomous vehicles to incident locations to capture moving-scene images when no detector was present.

Matches vehicle state with operator proficiency and arousal to improve remote monitoring assignment accuracy and stability.

Body sensor data is carried on the chassis J1939 network to cut separate wiring and enable function control using gear and engine RPM.

Dual magnetic detection units match road-marker spacing and polarity patterns to reject external magnetic disturbances and cut false detections.

Packages move through bus stops or other public terminals before drone drop-off, cutting last-mile cost, congestion, and drone traffic.

Past support outcomes are used to filter robot attention requests and adjust alert output, reducing operator load without missing needed support.

Detects hard-to-pave edge changes and excluded zones so road paving machines can correct target areas and maintain paving quality.

By excluding hard-to-pave edge regions based on road shape and machine speed, this case improves paving coverage and construction quality.

Adaptive speed limits use predicted link quality and traffic conditions to reduce tele-operation delay risk while preserving vehicle efficiency.

Dynamic speed changes on planned sorting routes help vehicles avoid collisions, jams, and livelocks while sustaining throughput.

Global and local self-learning models improve fleet arrival and energy predictions by adapting to route conditions and vehicle-specific deviations.

Stationary landmark matching with a radar reference map enables sub-meter vehicle pose using low-cost radar and lower-grade navigation systems.

Incident-angle checks against surface normals filter shallow laser scans, reducing false object readings and improving mobile robot occupied maps.

A swipe-based challenge response keeps remote parking active only during deliberate touch input, braking the vehicle when the user freezes.

A touch-screen challenge-response keeps remote parking active only during valid rub gestures, and brakes the vehicle if input stops or fails.

Timed image comparison and route planning let movable cameras detect illegally parked vehicles with less manual patrol effort.

Distributed route calculation and beacon redirection cut ATN communication bottlenecks while improving scalability and priority vehicle handling.

When a contingency event interrupts robot travel, this case shows how position and travel-time data guide alternate destination selection.

GPU-accelerated encoding over private LTE or 5G cuts remote video delay, enabling faster operator intervention for vehicles, robots, and drones.

A cellular network node suggests safer teleoperation routes when coverage is insufficient, reducing remote fleet interruptions and collision risk.

A service provider coordinates robot status and traffic signal requests so delivery robots can access urban infrastructure with fewer delays.

Position coordinates are encoded as visible patterns between drones, avoiding radio jamming while supporting secure spacing and collision avoidance.

Roadside sensors relay road environment data so remote vehicle control can preserve decision accuracy while reducing 5G uplink bandwidth use.

A virtual tracking target lets a mobile robot switch modes to keep people in view through lateral motion, corners, and narrow passages.

Image-based obstacle detection updates passage availability in real time, helping moving bodies avoid temporary warehouse obstructions.

Peer vehicle motion data flags wind and road-surface effects so IMU readings can be filtered for more accurate dead reckoning.

Integrated Bluetooth and RFID beacon IDs keep fleet vehicles trackable and controllable in caves, warehouses, and other GPS-denied areas.

Geofence and GPS checkpoint sequencing improves haul-truck cycle time accuracy while filtering false detections for better resource allocation.

When a robot hits a contingency event, this case shows how position and travel-time data guide alternate destination selection for safe navigation.

By mapping object occlusion zones in a 3D waiting area, the robot repositions to keep traffic lights visible before crossing.

A central controller uses position-timestamp schedules to split and rejoin virtual ride trains while keeping vehicle spacing synchronized.

Package priority levels let autonomous delivery robots pass bottlenecks in the right order, reducing congestion and urgent delivery delays.

Intersection passage priority is scheduled for unmanned vehicles to cut loader idle time and reduce loading loss on multi-loader work sites.

Predicting passenger arrival order from flight and baggage factors helps sequence vehicle unloading and reduce curbside vehicle dwell time.

Public transport terminals serve as transfer and charging hubs, extending drone range, lowering delivery cost, and easing drone traffic.

Pavement data is turned into map-based forecasts of material depletion and completion points, helping paving crews time work and mixture supply.

Adds vehicles only when real operating intervals exceed a demand-based threshold, improving service frequency without unnecessary fleet complexity.

Vehicles map strong magnetic fields with position data and share hazard alerts to support rerouting and protect sensitive sensors.

An AI virtual agent fills missing scheduling parameters and updates fleet delivery plans in real time to reduce manual errors and rework.

Sensor-based emergency detection selects an assistance location and notifies the nearest emergency vehicle to guide autonomous vehicle response.

A trained classifier predicts deadlock-prone heavy-duty vehicle trajectories early, enabling route adjustments to prevent congestion.

Two-model agent movement control reduces delay and energy variation across nearby agents, improving fairer multi-agent navigation.

By identifying manned vehicle discharge positions, the control logic reroutes unmanned vehicles to avoid path overlap and collisions.

Staggered fleet starts and feedback-based progress remapping reduce bottleneck queuing while maintaining uniform vehicle flow and productivity.

PI control at selected driver nodes suppresses misbehaving vehicle effects in directed multiagent networks without full-node feedback.