Road Vehicles Traffic Control TRIZ Cases

Multiple strain sensors inside the tire detect road steps from tire deformation, avoiding camera failures caused by water, mud, and weather.

Interior sound is modulated by estimated driving and surrounding conditions so EV drivers hear both pseudo engine presence and environmental cues.

Automated roadside image capture and local processing improve sign inspection accuracy while cutting manual surveys, transmission load, and cost.

Switching from deceleration to stationary-hold commands using velocity and standstill signals prevents post-stop acceleration and stabilizes brake hold.

V2X-shared emergency alerts and projected road guidance help nearby vehicles clear a safe route when sirens are occluded or out of range.

Sensor data from the subject vehicle detects roadwork items and nearby vehicles to set a traffic-aware construction-zone end point.

Sensor and map assessment lets a sidewalk robot cross roads autonomously when safe and hand off risky crossings to an operator.

A stacked autoencoder predicts safe and unsafe lane maneuvers from sensor data, helping autonomous vehicles react earlier and safer.

A stored map of stationary objects captured during parking-in helps the vehicle detect obstacles more reliably when exiting at very low speed.

Trajectory variability and multipoint-turn data help guide heavy-duty vehicles through narrow urban road segments with high driving difficulty.

Speed is adjusted by recognized road type and travel direction, allowing safer movement on direction-limited roadways and nearby non-road areas.

Combining sign detection, road markings, GPS, and mapped road context prevents false speed-limit warnings for detours.

Vehicle ambient data is mapped to road surface temperature, improving estimation where roadside weather stations are sparse or unavailable.

By combining lane centerlines with past motion and IOC ranking, this case improves trajectory accuracy and diversity for autonomous driving.

Trajectory planning widens actual sensor detection range around recognized objects, improving blind-spot safety without unnecessary deceleration.

On-board perception builds virtual parking strips from signs, markings, and vehicle motion to handle outdated maps and temporary rule changes.

Pre-activating recorded maneuvers before arrival reduces driver distraction and enables smooth automated trajectory-based parking.

Animated windshield visuals show wind direction, speed, and vehicle impact so drivers can grasp unstable behavior risks in real time.

Phase-based steering damping avoids weak initial steering and suppresses return overshoot, helping vehicles clear obstacles while staying in lane.

Fused camera views and distance sensing identify open parking spaces, plan a vehicle path, and reduce driver intervention.

Multiple road-image models trained for different weather and time conditions are combined to improve road state determination accuracy.

Crowdsourced vehicle sensor uploads let the cloud correct road maps in real time, cutting collection cost while improving update precision.

Adaptive action timing based on target motion helps autonomous vehicles break pedestrian or flagman stalemates and keep traffic moving.

Radar detection-point clustering lets the vehicle recognize travel-restricted road sections without object recognition, helping prevent unsafe lane changes.

Detects coordinated threatening vehicle behavior and triggers evasive maneuvers, deterrence, or escape routing to improve response in road blockades.

Occupant pickup updates trigger automated vehicle moves to nearby vacant event parking, improving access while coordinating secure spot assignment.

Environmental-condition grouping helps anomaly detection separate normal driving variability from unsafe behavior, reducing false positives.

Surrounding vehicle and occupant data is turned into in-car displays and games to reduce boredom during autonomous trips.

Combining heterogeneous sensor detections into one measurement grid cuts occupancy-grid processing latency while preserving object detection accuracy.

Sensor modules on crash attenuators detect minor impacts, send real-time alerts, and improve roadside asset inspection and repair.

Pre-installed lithium-ion backup keeps traffic signals running through outages with automatic switchover and remote status monitoring.

Pre-reserving stopping space and sharing it with nearby vehicles helps autonomous cars avoid abrupt stops, dooring conflicts, and whiplash risk.

Road sign recognition is filtered by vehicle attributes and position data to notify drivers of the traffic regulation that actually applies.

Road-aware exclusion areas filter sensor targets by vehicle position, cutting processing load while preserving responsive control.

Two processing paths use overlapping sensor sets to validate automated vehicle trajectories with lower compute load and better inconsistency detection.

At blocked intersections, autonomous vehicles share obstacle and path data to coordinate passage order and avoid indefinite standstills.

Combining road sign recognition with vehicle attributes and position helps notify drivers of the speed limit that actually applies to their vehicle.

Combining diffusion and VAE occupancy decoding improves object track prediction in dynamic scenes for safer autonomous vehicle planning.

Sensor-based position tracking and AR cues help vehicle groups maintain uniform spacing and formation with less driver distraction.

Real-time sensor mapping lets a forest machine recognize tracks and steer accordingly, reducing separate surveying work, delays, and tree damage.

Shared data from other vehicles lets a car anticipate road, traffic, and weather conditions and adjust driving mode before onboard sensors detect them.

Helmet sensors and blind-spot imaging detect nearby vehicles without head turning, improving rider awareness and stability.

Server-computed 3D occlusion data helps autonomous vehicles detect sensor blind spots accurately while limiting transmitted data.

Displays a recommended following gap from traffic light timing and vehicle spacing so drivers can follow safely and avoid unnecessary stops.

Real-time comparison of road and vehicle operating parameters keeps automated driving within its defined ODD and prevents unsafe out-of-domain operation.

Intent scoring and nearby vehicle identification let a horn send messages only to the intended vehicle, reducing noise and confusion.

Modulated vehicle lights are fused with V2X RF messages to match camera objects more accurately despite lighting and location errors.

User terminals relay event commands to boarding mobile objects based on position, cutting relay hardware while keeping event operations coordinated.

Wearable transceivers and radar retroreflectors help vehicles detect people and animals more reliably in darkness and adverse weather.

When abnormal communication is detected in one autonomous vehicle, fleet-wide or vehicle-only stop control protects users while limiting service disruption.