Arrangements For Variable Traffic Instructions TRIZ Cases

Adaptive movement-region control distinguishes stationary from moving pedestrians to avoid unnecessary path blocking and rerouting.

Calculates driver-specific takeover timing from secondary tasks and biological signals to avoid emergency stops and keep traffic moving.

Driving environment matching lets vehicles share only compatible autonomous driving algorithms, improving service quality across conditions.

Bicycle marks, boundary objects, and road width help distinguish roadway from sidewalk so a mobile body can adjust speed and mode safely.

Road-section regulatory map data lets autonomous vehicles adjust driving functions by location and conditions, improving compliance and safety.

Sensor-based abnormal driving detection is paired with spoken driver feedback to adjust assistance and improve support suitability.

When obstacles stop an autonomous vehicle, the control system replans a bypass path with curvature limits to reduce delay and maintain stability.

Lighted buttons and wireless communication let separated vehicle operators send clear visual alerts when voice or line-of-sight signaling is difficult.

Roadway, road user, and weather data are used to predict missing or obscured traffic signs, improving ADAS navigation reliability.

Feasibility filtering and adaptive search depth help centralized autonomous vehicle planning use limited compute more efficiently as fleet size grows.

Separate OS layers and a hypervisor isolate travel control calculation from execution, improving vehicle control stability during OS trouble.

By predicting which autonomous vehicle enters a risk area first, the system assigns a trailing follower to maintain reliable remote operation and safety.

Separate inner and outer area prediction improves moving-object behavior estimates and supports more accurate vehicle behavior and trajectory planning.

When center links drop, expected restoration time guides whether autonomous travel continues and whether local assistants are notified.

Temporary keys and server-side drive plan checks enable autonomous valet parking with lower parking-lot equipment complexity and safer route execution.

Remote advanced preprocessing compares with onboard results to improve autonomous travel control without adding vehicle power or space demands.

Overlapping exclusive areas around vehicles, bicycles, and pedestrians cut false braking while improving collision alerts for mixed traffic.

A safety verification layer checks third-party control commands against the mobile body's current state before execution.

Recognition feedback from nearby vehicles or infrastructure triggers speed, lighting, or data changes to improve own-vehicle detection in poor visibility.

Predicting vehicle behavior deviations and adjusting safe distances helps autonomous driving avoid collisions and assess accident responsibility.

Sensor priority is set from vehicle motion and surroundings so critical camera and sensor data keeps higher transmission quality under bandwidth limits.

Roadside detection data joins camera and radar inputs to identify sensor faults without adding onboard sensors or cost.

Sensors and processors choose the exit lane and turn so an automated parking maneuver enters traffic in the correct direction.

Balances sensor analysis depth against allowable and transfer delays to deliver timely driving support as a vehicle approaches dynamic objects.

Real-time lane-state assessment adjusts main-road vehicle acceleration to create safe merge gaps and reduce side-road congestion.

Advanced travel-control preprocessing is shifted from onboard computers to external servers to improve autonomous driving safety, speed, and delay.

Vehicles share position and passage-time data to adjust speed through narrow single-track roads, avoiding collisions, reversing, and delay.