Arrangements For Variable Traffic Instructions TRIZ Cases

External abnormality detection lets an autonomous travel controller bypass center approval and hand movement control to the user when urgency is high.

External sensor coverage and non-measurement zones are overlaid on maps or real-space images to help drivers interpret blind intersections.

Onboard surroundings sensors compare vehicle heading with lane direction to detect wrong-way driving without GPS or map errors.

Direct V2V data enables driving assist, while server-delivered vehicle data is limited to driver alerts to avoid unsafe automatic control.

Broadcast keep-out-zone coordinates from a located transmitter so vehicles can avoid accident or construction hazards as boundaries update in real time.

Fuses onboard sensing, map data, and nearby vehicle sensor feeds to present a clearer road view when fog, rain, or smoke impairs driving.

Point-based road-type recognition distinguishes roadway from sidewalk-like regions so mobile bodies can apply the right speed limit.

Cross-checking communicated and sensed driving data helps identify fraudulent vehicles while reducing manual verification and claims delays.

Severity scoring and risk-area grouping let connected vehicles choose coordinated or individual maneuvers to reduce exposure to unsafe driving threats.

By analyzing preceding vehicle trajectories, the controller finds a collision-free zone beyond the stop line to let emergency vehicles pass safely.

Dynamic smart-contract distance rules adapt to vehicle context and passenger health to improve autonomous traffic safety and security.

V2X-based vehicle detection helps distinguish manually driven cars in poor visibility, enabling safer spacing and smoother platooning.

When doors or windows hide body-mounted images, control shifts or resizes content across visible vehicle panels to maintain display effect at lower cost.

Pre-registered assistance points and influence areas let autonomous vehicles defer unnecessary remote requests and avoid inappropriate operator instructions.

Area sensing and reference comparison verify real obstacles before automated parking guidance, reducing false stops and collision risk.

Real-time wiper and rain sensor signals from connected vehicles let a backend detect local precipitation and alert only relevant drivers.

Notifying nearby vehicles of consecutive planned maneuvers helps them anticipate lane changes, merges, and turns to maintain safe distance.

Brake light detection is cross-checked with headlight state and vehicle deceleration to avoid false braking and improve following control.

Real-time trouble data is sent to an external controller to generate and implement countermeasures for tire, battery, and travel hazards.

Shared recognition levels from nearby vehicles or infrastructure trigger speed, lighting, or data changes to improve vehicle detectability.

Beacon radio signals and antenna orientation enable fast, precise vehicle positioning in narrow areas without repeated camera or sonar remapping.

Traffic light timing, vehicle position, and driving state are combined to stop distracting services while preserving use during predictable stops.

Low-similarity driving scenes are matched with detected risk events to expand a vehicle ontology and cover previously unseen collision risks.

A moving attention zone expands with vehicle state to warn following cars earlier while limiting unnecessary alerts.

Operator muting lets an autonomous vehicle bypass false obstacle stops at reduced speed, preserving path traversal without ignoring real hazards.

Adaptive merging control shifts intervention to the vehicle with stronger wireless reliability, helping prevent lane deviation and collisions.

Preloaded waypoints and control values let vehicles maintain smooth, safe merging travel even when server communication is temporarily interrupted.

Human-like speed planning uses fused driving features and lateral decisions to handle conflict uncertainty while improving traffic flow and safety.

Fused driving features and lateral decisions infer competing or yielding intent to recommend safer, smoother vehicle speeds.

Centralized platoon admission checks driver qualifications and vehicle compatibility to organize safer, more efficient convoy travel.

Vehicles share readiness and movement range in generic coordination messages, enabling interoperable maneuver planning across manufacturers.

Warning intensity is adjusted by checking whether the driver has already seen a passerby, reducing distraction without missing collision risk.

Pre-checking obstacles along a recorded parking route helps determine drivability before auto parking and avoids blocked vehicle movement.

Arithmetic models are assigned by driving level so mixed-automation vehicles can register compatible assistance and start level-appropriate control.

Recent driver operations are verified before automated driving starts, reducing erroneous starts while avoiding extra switching steps.

Operational and infrastructure data are combined to map risk areas more accurately and regulate vehicle routes for safer automated driving.

Predicted communication quality guides operation mode selection and travel speed control so remote operators can react in time and avoid obstacles.

Position-based selection of onboard and roadside sensor data improves target detection accuracy and vehicle control reliability.

Probe vehicle data pinpoints priority-road intersections with low risk reduction, enabling earlier brake support where visibility is obstructed.

Repeater-based wireless links let remote operators monitor vehicle health, receive failure alerts, and coordinate response vehicles beyond local range.

When cameras or V2X fail at intersections, surrounding vehicle and pedestrian behavior is used to infer traffic light status for safer autonomous driving.

Pedestrian arrival times are estimated from onboard imaging and shared between vehicles to warn of intersection conflicts in poor visibility.

By detecting pedestrian signal changes ahead, the vehicle predicts stop-signal timing to avoid sudden braking at intersections.

Intersection-aware steering angle setting helps autonomous vehicles restart smoothly after temporary stops and complete turns more efficiently.

Stores sensor data, liability rules, and driving state records together to verify accident liability and distinguish automated from manual driving.

By detecting another vehicle's lane-change intent, the host vehicle can target the gap it creates and complete lane changes in tight merges.

Traffic-rule verification filters illegal vehicle traces before map updates, improving road coverage without corrupting map accuracy.

When map-generation and operating conditions differ, non-map sensor data helps autonomous vehicles maintain accurate position estimation.

Surrounding sensors detect contact, sound, images, and emergency approach signals to trigger external braking when a driver becomes abnormal.

A height-aware display compares the set vehicle height with upcoming clearance signs to warn only when loaded cargo exceeds the limit.

Pre-collected vehicle characteristics are matched at a route checkpoint to trigger timely entry restrictions or guidance in automatic valet parking.

A single ECU with interior and exterior cameras consolidates vehicle services to cut ECU count, save space, and preserve fuel efficiency.

Dynamic function restrictions based on communication quality and nearby conditions help remote operators avoid unsafe autonomous vehicle actions.

Visual patterns captured by a drive recorder are relayed on a rear display, enabling immediate vehicle-to-vehicle information transfer without radio waves.

User presence detection wakes a parked vehicle's radio only when occupants stay inside or nearby, preserving battery while enabling emergency alerts.

Signal intensity from in-vehicle internal and external communicators lets a terminal detect whether it is inside or outside a vehicle without pre-association.

Object detection adjusts the excavator arm's movable range in real time to avoid nearby shovel collisions without unnecessary slowdowns.

By recognizing merge points early and shifting vehicle position relative to main-road traffic, automated driving can continue through merges with less driver takeover.

Superimposing received display data on a nearby vehicle's real-world position helps drivers quickly identify cars available for inter-vehicle communication.

Object overlap over time ranks which vehicle sensor detections to send first, reducing data overload and communication faults during driving assistance.

When anomalies occur, reliability checks and pre-obtained safe-area data guide the robot to stop in a safer location with less environmental risk.

Map-based image checks identify vehicles or buildings blocking route landmarks, helping users regain visual guidance with less confusion.

Autonomous vehicle system predicts oncoming traffic behavior to navigate narrow passages safely.

A system generates an in-car view of traffic conditions from a driver's perspective using virtual vehicle interior elements.