Instruments For Road Network Navigation TRIZ Cases

Semantic map correlation checks sensor observations against expected road features to detect unreliable vehicle pose estimates in autonomous driving.

Static and dynamic road data are fused to score candidate routes across multiple moving agents, improving path planning beyond short time windows.

Interpolating between traffic-constrained and free-speed averages improves route speed prediction, ETA accuracy, and vehicle energy management.

Route simulation across traffic, grade, and curvature predicts battery SOC by destination, improving EV range confidence and battery use.

Precomputed grade-based driving constraints help autonomous vehicles cut fuel use, reduce component wear, and avoid unsafe route actions.

Scaled vehicle tests and multi-dimensional scenarios quantify how ADAS features change accident probability for more consistent risk scoring.

Aggregated trajectories from harvesting vehicles are aligned and normalized to map drivable paths with less data and better navigation accuracy.

Aggregated drive data from multiple vehicles is converted into road-topography images to generate drivable paths with lower map storage and processing load.

Combining accelerometer and GPS time-series with gradient boosting improves transport mode recognition while limiting GPS energy use.

Route guidance combines battery consumption checks with GPS and visual landmarks to keep battery-operated vehicles from stopping before arrival.

Predicts a leading vehicle's stop point near an exit so the following vehicle can avoid blocking rear vehicles entering side roads.

Detailed map data is sent only to difficult locations flagged by vehicle cancel reports, cutting bandwidth while reducing autonomous control interruptions.

Transfers a learned parking trajectory between vehicles by adapting offsets, steering, and sensor-based parameters to avoid parking aborts.

Stored camera views fill hidden parking areas on the trajectory map, helping drivers judge obstacles and adjust automated maneuvers.

Visual targets and onboard imaging replace costly, low-precision GPS to map small or indoor sites and automate implement positioning.

Nearby EVs are matched to assist low-charge or stranded vehicles, reducing charging delays without relying only on fixed stations.

GPS and MEMS phone data detect braking, speeding, and acceleration to predict fuel use without OBDII hardware or delayed trip feedback.

Routes EVs to compatible charging stations using SOC, real-time availability, and a chargeable index to cut search time and failed stops.

Aggregated AV fleet data links software versions with environmental conditions to flag trip-specific adverse outcomes and trigger remedial actions.

A vehicle calculates spare travel energy to automatically charge a portable power source without reducing range to the set destination.

At temporary intersection stops, steering angle is set by intersection size to ease turn restart while limiting obstruction and wear.

Vehicle-mounted sensors and trajectory generation automate driving data labeling, cutting manual collection time and improving neural network training accuracy.

Sensor-driven AI ranks vehicle alerts and voice prompts so critical messages get through without disrupting conversations or distracting users.

Feature-point map alignment corrects and verifies object positions from other vehicles before sensor fusion affects safety and automated driving.

Precomputed charging-station graphs and nonlinear charge curves help EV navigation minimize total trip time while matching station compatibility.

Sensor, location, and image overlays help drivers spot and reserve parking spaces faster, cutting search time and urban congestion.

Mobile vehicle compute nodes are assigned near demand hotspots to cut cloud latency and improve edge resource allocation for AR and VR tasks.

Food and beverage orders trigger matched lighting, climate, audio, and display control to create a better in-vehicle dining environment.

Sensor-based road boundary and lane checks detect HD map mismatches, helping autonomous vehicles avoid unsafe operation.

Route segments are ranked for terrain changes using terrain and battery history to improve machine battery usage and health.

A BEV uses DC-DC conversion, pre-charge timing, and connection detection to jump start an ICE vehicle without damaging its own battery.

Road-class and vehicle-type segmentation improves real-time autonomous vehicle coverage analysis and supports safer navigation alerts.

Top-down ML mapping combines sensor and map data to identify temporary drivable space and guide safer autonomous vehicle trajectories.

Multi-view BEV features and memory tokens preserve static road maps under occlusion, improving lane keeping and path planning.

Threshold-based concavity segmentation turns drivable area boundaries into logical target zones for predicting pedestrian and cyclist paths.

Real-time image analysis in moving vehicles identifies open parking spaces and alerts nearby drivers without processing every scene in full.

Discrete road-segment profile matching localizes vehicles accurately while cutting continuous bandwidth and computation demands.

Centralized fusion ranks static roadside objects to align multi-vehicle perception data, cut artifacts, and ease onboard computing.

Centralized bound storage and node pruning cut memory and computation in autonomous driving trajectory planning under dense constraints.

Route and speed profile cues show why assisted driving changes speed ahead, reducing driver disorientation and improving confidence.

A drone scans beyond vehicle sensor range to find accessible charging stations faster, reducing EV search time and energy use.

Destination guidance shifts between centered and scene-aligned images to preserve the forward view while keeping orientation cues clear.

Adaptive split-screen display sizing balances map context and road condition visibility to improve ADAS control understanding with lower processing load.

Route-based regional map updates keep autonomous driving data current before entry, avoiding slow full refreshes and reducing safety risks.

When a parked vehicle stops mid-route due to an obstacle, remote path replanning moves it to a user-specified accessible location.

Loaded vehicles are barred from stopping at selected nodes, cutting uphill restart energy, wear, and traffic contention.

Preplanned transition points and backup trajectories help autonomous vehicles hand over safely on narrow roads with sensor blind spots.

Real-time coordinate comparison links general and precision maps to select local paths without matching-table updates, reducing control overhead.

Route search adds automatic car wash stops based on wash interval and travel distance, keeping autonomous vehicles clean without major service disruption.

During disasters, unmanned vehicles move to refuge positions that avoid blocking evacuees and help prevent entry into dangerous zones.