Road Vehicles Traffic Control TRIZ Cases

Geospatial and real-time vehicle data are fused to apply steering torque that offsets crosswinds, road banking, and load-induced drift.

Off-screen object indicators and segmented vehicle UI regions reduce overload while preserving operator awareness for safer decisions.

Uses weighted lane safety, relative distance, and time-to-collision to choose the safest stopping lane during autonomous minimum risk maneuvers.

3D sensor mapping detects unsafe reversing speed, angle, and weak barriers near cliffs, then warns or constrains vehicle motion.

Shared vehicle path and obstacle data let a server build parking lot maps for automatic parking without large onboard memory.

Uses lateral position and drift speed mapping to adapt lane-keeping correction, reducing control complexity and oscillation.

Sensor-based corridor analysis lets autonomous vehicles detect unplanned traffic redirections and follow the correct traffic flow safely.

Vehicle size and turning data are used to assign suitable parking positions and set fees more accurately in mixed-spec accommodation areas.

Filtered sensor matching removes noisy object detections from site maps, improving work machine positioning when GNSS accuracy drops.

When sensor uncertainty or communication lag rises, remote piloting is restricted to prevent unsafe autonomous vehicle movements.

Selecting multiple candidate autonomous vehicles and routes improves arrival reliability at dispatch points despite changing traffic.

Accumulated encounter-location risk and other-vehicle motion prediction help vehicles avoid hazards before detection and reduce unnecessary stops.

A controlled rapid-then-slow guard rail approach dissipates vehicle kinetic energy, reduces rebound, and limits crash damage.

Tactile alerts using air blasts, thermal radiation, and vibration help warn distracted pedestrians and cyclists of vehicle movement.

Evaluates surrounding traffic risk by combining vehicle size, motion, and environment data with 2D overlap regions to support collision avoidance.

Speed-triggered smartphone checks use biometrics and ride-app data to tell whether a restricted individual is driving or riding.

Warnings are issued only when the next light is near green and the driver is inattentive, reducing unnecessary alerts and congestion.

Distance-based display logic keeps construction site speed limits visible until the vehicle moves far enough to safely switch back to database speeds.

Parking-situation data is used to rank permissible target positions, speeding autonomous unparking while reducing user input and wasted movement.

Geofenced lane and heading data isolate the relevant intersection signal, improving autonomous driving control during lane changes.

A modular SoC splits camera, RADAR, LIDAR, and map processing across specialized processors to improve redundancy, safety, and power efficiency.

Dynamic reference distances let fleet-managed autonomous vehicles coordinate with nearby target objects while balancing responsiveness and plan consistency.

Position coordinates are encoded as displayed light patterns that another mover photographs and decodes, avoiding radio jamming during control.

Operator skill and attribute levels are used to adjust nearby traffic notifications, improving safe and smooth moving-body control.

Roadgraph node and edge overlap tracking detects autonomous vehicle loops early, enabling rerouting or remote assistance to avoid wasted trip time.

Timed image comparison and patrol routing help detect illegally parked vehicles with less manual patrol and stronger enforcement.

Consolidated sensor data and trust-rated object lists let autonomous vehicles adapt safety responses in mixed warehouse traffic.

Low-frequency magnetic field markers localize vehicle detection at facility points of interest, reducing centralized tracking complexity and safety risks.

Dynamic moving position-targets reroute autonomous vehicles around exclusion areas so priority vehicles can pass with less collision risk and delay.

Pacing directives with speed, throttle, and braking limits keep vehicles on schedule while improving route network throughput.

Automatically updated driving-event queries and map abstractions keep scenario likelihood and safety metrics accurate without heavy raw-data searches.

Adaptive sensor and video modulation cuts teleoperation latency by matching virtual environment data to network and environmental conditions.

By partitioning vehicles around MUTEX dependencies, this case cuts trajectory-planning complexity while preserving safe crossing order.

Vehicles use location, orientation, and zone policy data to start speed or height adjustments before the entry edge, improving compliance and flow.

A variable loading platform and telescopic side walls let an autonomous transport vehicle fit changing aisle widths and object sizes.

A two-stage display guides touch and rotation input for remote vehicle movement control, improving viewing clarity and operation precision.

Dynamic teleoperator-vehicle matching improves remote driving fleet productivity, safety, and task coordination across changing conditions.

Collected parking data sets bays to insert-ready or retrieve-ready states, helping AGV parking systems cut retrieval time while keeping high density.

Tracked route overlap helps autonomous vehicles detect repeated loops early and trigger rerouting or remote assistance.

Static analysis of AGV path nodes, edges, and travel rules identifies deadlock states faster and more completely than simulation.

Qualification and plan attributes are checked before mobile body operation requests are accepted, improving safety and management efficiency.

Multiple sensor array lines cross the marker path so road markers stay detectable during forward or lateral vehicle movement.

Cost-based path assignment selects the best standby vehicle and adjusts routes to reduce congestion, waiting time, and deadlock.

Short-range transponders let an autonomous vehicle detect zone boundaries and enforce speed or steering limits without continuous network connectivity.

Route-point envelopes in a tunnel model help operators monitor autonomous underground vehicles, spot critical points, and manage traffic proactively.

Traffic-rule nodes guide agents by region to avoid runtime inter-agent communication, easing congestion and computational load in real-time navigation.

Vehicle telemetry and user inputs let a UAV predict car movement and coordinate flight without handheld controllers or added hardware.

Road surface maps and sprinkling data predict puddles on mine roads, enabling route changes that prevent skidding and work delays.

Safe-stop route segments let autonomous vehicles accept remote guidance despite latency or message loss, preserving safe traversal.

Fused data from multiple monitoring units creates richer object lists, helping AGVs avoid hazards and keep operating through sensor errors.