Segmented visual output indicates steering direction and object proximity, resolving information completeness versus device complexity contradictions.
A vehicle monitor displays superimposing guide lines with periodically altered colors to maintain a small footprint on the captured image.
Processor exchanges detection data between multiple light emitting and receiving elements to maintain accurate signal output.
Radar systems estimate vehicle speed using angular position data and Doppler shifts, bypassing tire rotation errors and GPS signal loss.
A vehicle control device sets a dynamic standby time period based on detected traffic participants before allowing entry into a railway crossing.
Automated video system classifies stationary vehicles as double parked using hazard light and traffic pattern analysis.
A vehicle monitoring apparatus extracts head and leg image portions using reference mask areas to identify pedestrians accurately.
A vehicle platoon control system coordinates movement of road vehicles as a variable shape body using wireless communication and sensor data.
A driving assistance apparatus coordinates acceleration and deceleration support using a predicting section to adjust assistance levels.
Behavioral prediction algorithms assess collision threats to increase time for evasive maneuvers during cut-in events.
A TPMS controller selects sensors based on parking path data to collect pressure measurements during vehicle maneuvers.
A wireless push notification system transmits pedestrian motion data to an autonomous vehicle receiver.
A vehicle driving assistance system selects driver type and senses condition to control functions adaptively.
A vehicle alert system transmits transient condition data to proximate vehicles via V2X communication modules.
A vehicle-mounted parking memory system stores and recalls spatial distances to facilitate precise positioning during subsequent operations.
Radar tags encode traffic data via dynamic reflectors, allowing sensors to detect signals and reduce hazards caused by driver distraction.
Offloading image analysis to a host computer resolves hardware constraints, enabling high-speed object detection and seamless software updates.
A control system combines sensor data into object lists and uses LSTM networks to project future locations of dynamic objects.
Selective high-resolution sensor processing for vehicle environment models resolves computational complexity bottlenecks in real-time object detection.
A drowsiness estimating device combines audio, image, and physiological signals to calculate individual sleep levels.
Circular holding patterns resolve imminent collisions in multi-vehicle teams while maintaining scalability for hundreds of robots.
Angular velocity sensors detect head and toe movements to identify visually checking motions and braking stances in vehicles.
In-vehicle computing system captures image data from neighboring vehicles to identify erratic driving behaviors.
Access network components receive terminal data and transmit it directly to selected devices via multicast or broadcast channels.
A vehicle ultrasonic sensor uses a band pass filter and 2N decimation to shift signal frequencies.
A flying scooter integrates an oxygen generator and photovoltaic cells to produce breathable air while operating.
A driving control device sets a transition time-frame to gradually switch between lane change and lane keeping control.
A network-based facilitator system identifies speakers and retrieves relevant information to enhance user communication abilities.
A vehicle collision prevention system calculates time-to-collision metrics to manage driving operations.
A vehicle computer determines threat estimations using adaptive distance offsets and thresholds for lateral and longitudinal collision risks.
The system calculates dynamic likelihood thresholds from normal driving data to suppress erroneous degraded consciousness alarms caused by fixed constant values.
A controller filters recorded surroundings data to transmit only relevant subsets based on driving situations.
A vehicle travel control apparatus calculates virtual lanes and residual road width to determine optimal stopping positions for subject vehicles.
A world model includes hypothetical dark objects in unperceived areas to guide autonomous vehicle trajectories.
Adaptive control system calculates target speed and distance for automated lane changes.
A range changing unit adjusts an angle threshold value based on curve detection results before vehicle entry.
Driving controller detects opposing vehicle rear-lane entry to permit immediate intersection passage, reducing waiting time while maintaining collision safety.
An in-vehicle device detects curbstone shapes using sensor data to identify step elimination regions for safer travel.
Virtual springs connect an autonomous vehicle with surrounding objects to aggregate forces that resolve safety and complexity trade-offs during lane changes.
Periodic flashing patterns resolve safety distance contradictions by simulating potential slowdowns.
A vehicle control apparatus adjusts risk calculations based on parking states to prioritize warnings for nearby obstacles.
Beam splitters merge multiple coverage zones into one infrared detector, reducing device complexity while maintaining blind zone detection.
A vehicle-mounted equipment control device manages occupant drowsiness by adjusting audio and display settings based on real-time location data.