Road Vehicles Traffic Control TRIZ Cases

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Uncooked meals travel in insulated onboard ovens, with heat and humidity adjusted to delivery-time updates so food finishes cooking on arrival.

LiDAR-based route updates steer the mine vehicle around dust-created blind zones, reducing stops and maintaining hauling efficiency.

Off-board processing augments automated driving path planning, improving perception and path selection without raising on-board power or hardware cost.

By comparing driver takeovers with ADS maneuvers, the vehicle presents targeted cues before similar actions to build trust and reduce unnecessary overrides.

When a green light permits entry, the vehicle starts with tighter speed and acceleration limits if an oncoming turning motorcycle is detected.

Priority-based stop reservation management resolves overlapping vehicle schedules by adjusting stop times or positions to reduce congestion and rider disruption.

Image-based sign relevance filtering checks road position and side-road context so vehicle control uses only applicable speed limits.

Multiple cameras and machine learning detect workers or animals around a working machine and alert the operator during low-visibility maneuvers.

Virtual road users at sensor boundaries help autonomous vehicles predict crossing risk and set safer, smoother intersection approach speeds.

Predicting emergency vehicle appearances by route link lets autonomous vehicles adjust lanes or travel time to avoid schedule disruption and excess power use.

Counts dashed lane markers and combines host speed to detect preceding-vehicle deceleration accurately with a monocular camera.

Successive echo amplitude changes let a single vehicle ultrasonic sensor classify object height without extra cameras or sensor fusion.

Wireless traffic light state data and camera recognition let ACC stop or cruise correctly at signals, reducing violations and accident risk.

Brake intervention feedback helps an autonomous driving kit detect abnormal pedal operation while maintaining reliable control with the vehicle platform.

Driver gaze detection switches between display-only and sound alerts for rear vehicle approach, reducing annoyance and unnecessary eye shifts.

A school bus creates a virtual zone and sends child location alerts to nearby vehicles to improve exit and road-crossing safety.

By splitting road observations at lane-type branch points, this case improves lane association and continuous tracking where markings frequently change.

Wireless sharing of surrounding environment data lets detector-less straddle-type vehicles deliver rider assistance without added sensor mounting space.

Predicted collision zones are mapped into localized costmap changes, helping autonomous vehicles avoid dynamic multi-vehicle conflicts.

Roadside lane sensors and vehicle-mounted detection are combined to maintain a reliable travelling corridor at turns and in dim tunnels.

Estimated maneuver timing, position, and trajectory are sent with accuracy indicators so nearby vehicles can grant, reject, or counter safely.

Integrated control flows and logical constraints generate safety rules for complex multi-vehicle scenarios without manual branch explosion.

Real-time physiological and driving behavior signals are fused to predict prosocial intent and trigger vehicle feedback or assistance.

When no forward avoidance path exists, rear-image guidance warns of following vehicles or pedestrians before reversing on narrow roads.

Grid-defined subspaces replace all-pairs distance checks, enabling faster, scalable proximity notifications for many monitored devices.

Uses radar-based overlap change rate to distinguish passing vehicles from true rear-lane threats, reducing false warnings and improving avoidance.

Crowd-sourced danger heatmaps let autonomous vehicles adjust driving parameters at risky locations and respond to unknown hazards in real time.

Image-based parking-intent detection lets an autonomous vehicle identify parking spaces and yield or change lanes without blocking a forward vehicle.

Near the path endpoint, a speed guard caps path-point maximum speed to preserve accurate final stopping in automated valet parking.

Overlapping Ti and Si precursor supply forms a TiSiN barrier film that suppresses fluorine diffusion while reducing extra deposition steps.

Ranks non-urgent vehicle condition messages from driver response data and adjusts saliency to reduce confusion and improve awareness.

A merged path overlay marks where vehicle and trailer reach a target relative angle, making forward and reverse towing easier to understand.

By clustering vulnerable road users into moving groups, the vehicle predicts blocked regions and plans safer paths with lower computation.

Distinct forward and reverse trailer path overlays reduce guide-image confusion and make towing maneuvers easier to understand.

Driver behavior is used to calculate a relaxation score and suppress unnecessary alerts at selected automated-driving timings.

Pre-simulated and context-curated driving policies cut onboard compute load and selection latency for autonomous vehicles.

Combining surrounding vehicle speeds with map-based road factors yields a more complete driving scenario complexity measure for safer self-driving decisions.

Positive audible cues tied to vehicle data reinforce efficient driving habits while limiting continuous alerts and supporting EV battery life.

Leader-follower path prediction and forward evaluation improve collision risk assessment and trajectory planning in complex traffic.

Uses obstacle-vehicle block cost and speed-based weighting to make lane change decisions more stable, safe, and accurate.

Multiple lateral echo positions are trilaterated to separate double echoes and map recessed obstacle height for more accurate parking sensing.

Trajectory and lane-path intersection analysis suppresses false lane-departure warnings during intentional lane changes while preserving timely assistance.

Camera, radar, and V2X data let a host vehicle detect lower autonomy nearby vehicles and guide them with assistance or virtual lane lines.

Modular vehicle interiors and platform matching combine transport with meetings, exercise, or work to use travel time more effectively.

A designated reference vehicle lets autonomous driving wait for safe passage through a narrow area while cutting remote operator restraint time.

Cornering-aware following control changes inter-vehicle distance so a lean vehicle can keep the preceding vehicle in view and maintain safer spacing.

Multiple overlapping cameras and machine learning detect workers or animals around a working machine and alert the operator to avoid collisions.

Multi-input signal anomaly detection classifies faulty traffic lights and adapts autonomous vehicle control for safer intersection handling.

Planned trajectories are checked against nearby vehicles only when uncertainty or confidence thresholds require it, improving collision-free control with less data traffic.

Face-based boarding verification matches riders to reservations and boarding points to prevent incorrect boarding and assign seats accurately.

Varying driver response requests by sleepiness and driving conditions helps prevent habituation and can stop the vehicle if needed.

Optical amplification and coherent detection extend LIDAR range, improve low-reflectivity sensing, and enable direct velocity measurement.

Adaptive feature point updates keep automatic parking accurate when seasonal or time-based changes alter parking space recognition.

Existing cameras and external edge processing guide vehicle parking paths, avoiding costly lidar, radar, and ultrasonic infrastructure.

A wireless interface module relays warning-system activation signals to RSUs without traffic controllers, cutting power and installation needs.

Encoding training data into knowledge graphs and manifolds helps AI models traverse rare high-risk scenarios beyond rigid table schemas.

A scenario-tree planner delays route commitment until occlusions clear, balancing information gathering with risk-aware collision avoidance.

When a vehicle detects a safety envelope violation, scenario data is sent to a remote center for feedback that improves driving precision.

Adjusts collision intervention thresholds during turns using steering direction and lateral collision position to avoid false ADAS actions.