Instruments For Road Network Navigation TRIZ Cases

A roadway-aligned virtual reference keeps head-up display objects stable at the field edge during vehicle turns, avoiding erratic disappearance.

State-of-charge and trip-aware routing selects reachable charging stops and available stations to keep electric vehicles on course.

An autonomous vehicle carries and charges scooters or bikes to link trip legs, cutting AV mileage, coordination burden, and emissions.

Gyro-based azimuth control corrects lateral shift against magnetic markers, reducing placement error impact and supporting smoother travel.

A clutter map adds occupancy and velocity risk to static maps, helping autonomous vehicles avoid unseen obstacles with lower sensing load.

Multiple trajectory models are compared by error and confidence over time so autonomous vehicles can choose the best predictor for each object.

Accident modeling and filtered nearby driver behavior data improve vehicle risk prediction while limiting processing load and privacy exposure.

Filtering moving-object radar points and fusing prior frames with vehicle velocity improves autonomous vehicle localization in dust, fog, rain, and GPS-denied areas.

Find how destination-based search and booking of standard EV chargers cuts idle waiting by charging near transfer points or activity areas.

Trajectory planning adjusts the vehicle approach so the target parking space entrance stays within sensor range for accurate automated parking.

A server predicts battery use across wearable and mobility units to coordinate charging, routing, and reliable arrival at the destination.

Re-sampling and interpolation fill high-speed sampling gaps so each road-map subsection stores more accurate unsprung-mass displacement data.

Vehicle positioning data and pseudorange corrections help user devices localize accurately in GPS-blocked pickup areas.

Intermediate-point route curves account for vehicle steering limits and curvature continuity to avoid abrupt turns and improve ride comfort.

During congestion, the controller increases following distance after failed correction periods to recover road marking visibility and map position accuracy.

By evaluating lane-segment route candidates with A* and cost selection, this case avoids wrong junction paths in autonomous driving.

A server reprioritizes autonomous vehicle dispatch to recover lost articles quickly while preserving ride continuity for other passengers.

Sensor and historical road data are used to predict route damage, helping autonomous vehicles balance smoother travel with trip time.

When route image elements fall outside the windshield displayable range, the system truncates the AR guide to keep alignment natural and reduce driver discomfort.

A probabilistic lane planner uses expected time rewards and action success rates to avoid deterministic routing failures in traffic.

Millimeter-wave relay updates fuse shared HD map frames with onboard sensor data to extend coverage and cut latency for autonomous driving.

Adjacent lane-marking data is used to build an interim trajectory, keeping cruise control active through branching areas without clear white lines.

Global and local graph scoring helps a self-driving car choose lane paths that reduce unnecessary lane changes while adapting to real-time conditions.

A centralized outlet database verifies location and availability so users can quickly find and reserve compatible charging points.

Ground-based observer sensors locate vehicles along a route when GNSS signals are blocked, enabling accurate pre-movement position data.

Map-based communication sections let the vehicle switch between automated and driver control when signal loss would undermine control accuracy.

When a vehicle stops midway due to an obstacle, the system shares its path and position, then sets an alternative route to a specified spot.

A thin, wide traveling unit with detachable upper structures lets one automated vehicle switch between passenger, cargo, and support roles.

Exterior user identification lets people verify boarding or package loading permission on autonomous or remotely driven vehicles without an occupant present.

Reservation data lets the controller set vibration-sensitive travel characteristics before autonomous driving begins, improving occupant safety.

Captured speaker output is matched to reference ads so the vehicle media system can prompt navigation to associated business locations.

Ranks travel segments by safety, distance, and collision cost so vehicles can avoid obstacles and overloaded routes in complex road networks.

A cost matrix combines Mahalanobis distance and lane-marking type likelihoods to match perceived and mapped lane edges more accurately.

AR coupons are filtered by vehicle and user context, then shown in peripheral driving views to deliver location-based offers with less distraction.

Uses occupancy grids and precomputed motion primitives to plan collision-free vehicle paths when lane markings or signs are unreliable.

Routes an autonomous vehicle to another vehicle or user in real time, avoiding manual address entry when destinations change mid-trip.

Current weight and load distribution data let automated utility vehicles adapt trajectory planning for safer cornering and braking.

A vehicle control case that cuts trajectory planning effort by using fine local candidates and coarse long-range search for accurate updates.

Real-time weight and tilt sensing lets an intelligent pallet update routes and braking to prevent cargo falls on slopes.

Fusing IMU and wheel tachometer pose data with adjacent images improves autonomous vehicle positioning when camera vibration and interference reduce accuracy.

Remote trajectory selection helps autonomous vehicles keep moving when lane markings or runway lights are obscured, improving navigation safety and efficiency.

Map and sensor 3D positions are cross-checked to detect abnormalities while segmented data access cuts transmission load and preserves needed detail.

Dynamic thresholds and map-guided radar filtering improve adjacent-vehicle lane assignment and help prevent unsafe automated lane changes.

Bluetooth connection and location tracking estimate EV trip distance between charges, improving SoC prediction and charging power allocation.

Historical traffic data is turned into area collision risk indices that trigger autonomous control engagement or disengagement in hazardous zones.

Pre-rendering map screens for predicted route segments cuts real-time display delay and computational load in navigation.

When a charging vehicle nears its battery threshold, another vehicle takes over so the location keeps receiving power without interruption.