Arrangements For Variable Traffic Instructions TRIZ Cases

Position-based mode switching tags and forwards buffered vehicle data when coverage returns, reducing omissions without losing real-time monitoring.

Predicts when a stopped front vehicle will move so a car can time entry into an adjacent lane without missing short-lived gaps in traffic.

Road friction data from onboard sensors and external sources lets autonomous vehicles anticipate slippery sections and adjust driving strategy smoothly.

Antenna-aware driving and radio propagation simulation enables early V2X application evaluation without costly, risky vehicle tests.

Slowing the second autonomous lane change extends surrounding-traffic confirmation time and reduces risk during consecutive same-direction merges.

A roadside processor generates vehicle position and drive control data to cut onboard processor load and reduce self-driving latency.

Real-time virtual lane definitions adapt lane count and width around obstructions to improve roadway use and autonomous vehicle flow.

Traffic-state recognition keeps eyes-off automated driving active in sustained congestion and blocks starts when traffic may clear quickly.

Filtered roadside perception with effective-area data helps autonomous vehicles avoid blind-zone errors while cutting unnecessary data transmission.

Self-supervised change detection compares sensed objects with reference map features to trigger fast map updates for safer autonomous driving.