Road Vehicles Traffic Control TRIZ Cases

Detects deceleration alerts from preceding vehicles early, giving following drivers or braking control more time to avoid rear-end collisions.

Current onboard sensor checks validate cooperatively planned vehicle trajectories before execution, helping catch latency-driven safety risks.

By modulating directional audio cues from predicted object behavior and relevance, the system improves situational awareness without binary-alert annoyance.

Deep learning route candidates are accepted only inside a rule-based static safe area, raising autonomous driving safety toward ASIL-D.

Expected maneuver messages let vehicles receive coordinated driving instructions, moving V2X beyond alerts to prevent conflicts.

An arbitration-based brake controller resolves simultaneous driving support requests and feeds back vehicle motion to maintain stability.

Probe vehicle speed differences are used to split multi-exit lane links, improving lane-specific speed accuracy and arrival-time reliability.

Roadway segmentation lets vehicles adjust functions from cross-segment interactions, improving safety and traffic flow with secure data handling.

Detects emergency vehicles and switches to emergency-specific path planning so autonomous cars can yield quickly and comply with traffic rules.

Pre-checking adjacent and third-lane traffic lets an autonomous vehicle abort overtaking early and stay in lane to avoid conflicts.

Road magnetic markers and front-rear lateral shift sensing provide a more accurate absolute vehicle orientation reference than yaw or acceleration sensors.

By matching a target vehicle with similar sample vehicles, the system applies suitable settings for locations or environments it has not seen before.

AI siren detection on low-cost intersection hardware changes traffic lights in real time to clear emergency vehicles and reduce collision risk.

Cloud-assisted blind spot detection combines vehicle status, road conditions, and sensing capability data to reduce missed ADAS warnings.

Route-based vehicle allocation estimates arrival power and selects dispatch patterns that minimize travel consumption while meeting shelter demand.

Environmental data and parking history are used to recommend a safe vehicle pull-out direction with less user interaction.

Dynamic warning boundaries use target direction and radar location data to cut false rearward collision alerts without losing relevant coverage.

AI-driven electrochromic vehicle coatings display age and maintenance cues at test-zone boundaries to speed checkpoint inspection.

Bounding-box cut-in detection isolates difficult lane-entry events from driving data, cutting analysis cost while improving autonomous control training.

Sensors and image processing identify loads carried by working machines, then brake or reroute the vehicle to avoid passing underneath.

Segmented rotating or compressing disks in the steering wheel convey alert direction through localized tactile feedback for safer driver response.

External LIDAR sensors around the airport close towing tractor blind spots and improve high-speed vehicle position tracking to avoid collisions.

Fused ego and road-user occupancy grids improve long-horizon collision threat prediction in complex traffic while reducing false ADAS warnings.

Proximity and user-movement checks verify remote stop commands during autonomous parking, reducing misuse, traffic blockage, and abrupt braking.

Head movement and visual capability are used to map each driver's blind spot, improving warning accuracy for limited mobility or vision.

Probe data from multiple vehicles is turned into statistical trajectories, giving autonomous vehicles reliable route guidance through no-lane sections.

Spectral analysis of mobile magnetometer data improves transport mode classification while adaptive sampling balances accuracy and sensor energy use.

Driving data from nearby vehicles is filtered into wear-reducing feedback, helping drivers adopt habits that extend component life.

Display prominence changes by driving context so steering grip requests stay noticeable at autonomous-to-manual handover without constant driver annoyance.

Real-time sensor and contextual data trigger proactive driver alerts for distraction, hard braking, and other risky behaviors to improve safety.

Predicted road, weather, and traffic conditions let automated vehicles schedule ODD-compliant routes, reducing stops while maintaining safe operation.

GLAM scores and prunes graph edges before node embedding, reducing noisy connections and improving classification and regression inference.

Uses speeds from connected preceding vehicles to set host speed and mitigate phantom traffic even when few vehicles support V2X.

Detecting another vehicle's wheel angle helps predict low-speed lane changes earlier, improving control response in dense traffic.

RSU and MEC fusion data cross-check V2V vehicle location claims to detect ghost vehicles and prevent malicious messages from affecting ADS decisions.

Camera-based lane tracking suppresses false turn-signal warnings after a one-lane change while preserving alerts for adjacent-lane hazards.

Fleet intervention data pinpoints road locations where automated driving fails, enabling sensor correction and approval-zone mapping.

Priority-based V2X alerts share abnormal driver or vehicle status quickly while limiting network load and enabling fast intervention.