Road Vehicles Traffic Control TRIZ Cases

RTK-GNSS correction and roadside V2X relays enable factory shuttle autonomy with lower lidar cost, fewer driving errors, and safer transfer.

Multi-model simulation separates intent, goal-conditioned actions, and trajectory prediction to create realistic traffic behavior in new environments.

Adaptive sensor signatures and information carriers verify vehicle identity during fuel supply, blocking spoofed signals and unauthorized service.

Overlapping on-board and cloud control commands are arbitrated by urgency and data freshness to keep vehicle actions timely and reliable.

Multi-sensor lane change control uses route, camera, radar, and siren data to clear a safe path for approaching emergency vehicles.

Sensor fusion and machine learning estimate vehicle weight by path segment, improving grip determination when visual sensing is unreliable.

Multi-factor risk data is fused into N-dimensional safety areas to tailor driving warnings to individual habits and reduce alert overload.

Sensor data and heuristics classify malfunctioning traffic signals so autonomous vehicles can stop, proceed, or request assistance safely.

Lithium-ion backup keeps traffic signals powered through outages by switching within 50 ms and enabling stable grid reconnection.

Short-term route data is filtered into essential long-term points after arrival, cutting nonvolatile memory use while preserving guidance precision.

Fused road friction maps and object trajectory prediction help vehicles plan safer steering, braking, and acceleration on changing surfaces.

Real-time sensor risk assessment adds new driving scenarios when hazards do not match stored profiles, improving automated driving control.

Radio-frequency alerts reach drivers through in-car systems and phones when sirens and lights are blocked by sound isolation and privacy films.

Risk maps that separate road surface obstacles from other hazards help plan wheel tracks that avoid passable obstacles without unnatural vehicle meandering.

Candidate stop zones are scored by obstacles, nearby vehicles, occupant position, and vehicle posture to choose a safer bus stop point.

Predicted traffic stacks are handled by adjusting roadgraph edge costs to bias lane changes and keep autonomous vehicles moving through intersections.

When a parking-area base station fails, preloaded next-area driving data lets autonomous vehicles reroute and still reach the target spot.

Traffic light guidance is combined with inter-vehicle distance warnings to smooth intersection approach while reducing rear-end collision risk.

Predicts pedestrians and vehicles by destination and movement rules so the ego vehicle can adapt its path and avoid collisions in mixed traffic.

Selective sensor sharing filters personal identifiers and event data to support HD mapping and accurate vehicle path calculation.

Dynamic vehicle groups and attribute-based access control protect smart car resources with location-aware permissions and user privacy rules.

Planned switching points and speed control help drivers visually assess the manual travel zone before taking over from automated driving.

Road facility data extends motorcycle sensor range, enabling earlier adaptive cruise safety actions and reducing rider shock.

Rear-vehicle recognition triggers a higher automation state, cutting emergency vehicle response time while avoiding unnecessary mode changes.

Classifying steering intensity lets vehicle control intervene in μ-split shoulder driving to counter oversteer and return the vehicle safely.

A simulated cockpit lets remote operators take over autonomous vehicles during emergencies, improving response speed and control reliability.

Ambient data from vehicles is linked to roadside temperature references to estimate road surface conditions without dense weather station coverage.

Acceleration sensing and impact energy calculation reveal hidden crash guard damage, trigger alarms, and support preventive maintenance.

Progress-based height adjustment uses distance and time-to-collision data to improve obstacle footing and warn the driver earlier.

Road sign matching from onboard vision and V2V data improves vehicle position checks, cutting false collision warnings without high-accuracy maps.

Multiple cameras predict missing lane constraints and detect construction zones so driver-assist vehicles can adjust navigation when markings are unclear.

Combining onboard localization sensors with smart roadside sensing improves autonomous vehicle trajectory planning in weather, construction, and other non-standard conditions.

Radio device crowd tracking creates dynamic geofences that limit vehicle speed, steering, and access near dense pedestrian areas.

Road-surface pattern projection is coordinated with HUD status to avoid visual overlap, reduce eye movement, and improve driver information visibility.

Tripwire-triggered location updates let a low-power trailer asset tracker sync with tractor telematics and flag wrong trailer towing.

Virtual windshield images show wind direction, speed, and predicted instability so drivers can quickly grasp crosswind effects.

An RFID tag embedded inside a magnetic road marker adds road data for vehicle guidance while protecting the tag from surface damage.

Real-time hazard graphics and audible alerts show passengers how an autonomous vehicle detects danger and responds to avoid accidents.

Observer vehicles and infrastructure share motion data to predict occluded collisions and warn affected vehicles before impact.

Passenger type recognition lets an automated vehicle adjust pickup approach and stopping position to reduce fear and improve boarding safety.

Confidence checks on sensor-based traffic density help block unsafe automated lane changes when surrounding vehicle data is unreliable.

Route selection weighs travel time against remote assistance demand so autonomous vehicle dispatch can reduce remote operator workload.