Instruments For Road Network Navigation TRIZ Cases

Uses a road-boundary reference line and longitudinal arc length to map vehicle and target positions more accurately on curved roads.

When no curbside spot is available, the vehicle retries parking, loops around, or briefly stops in-lane to complete safer passenger pickup.

Layered map storage and live sensor fusion keep autonomous driving paths updated around moving objects while reducing data handling complexity.

Priority-ranked keep clear regions guide autonomous vehicle stop planning to avoid crosswalks, intersections, and rear-end collision risk.

Road-type gating composites onboard sensor and map lane data only on specific roads to improve lane matching and route accuracy.

Actual node passage times trigger delayed scheduling for following vehicles, preventing merge-point collisions without changing passage order.

Switching between 2D video and segmented 3D surrounding data improves random access and transmission efficiency under changing driving conditions.

Pre-mapped geofences and waypaths let autonomous vehicles handle parking areas reliably while avoiding complex street navigation.

User preference weights and viewpoint scoring let navigation choose leisure routes that balance points of interest with driving time.

Onboard processors preserve route enforcement targets and keep location-based vehicle actions running when off-board instructions are lost.

Color-coded in-vehicle displays highlight nearby objects and vehicle intent, reducing clutter while improving occupant understanding and confidence.

Precomputed off-road drivability ratings in lane-segment maps cut perception load and support safer fallback trajectories beyond sensor range.

Real-time lane activation and power allocation cut wasted roadway charging energy while matching EV demand, traffic, and weather.

Switching between 2D surround video and 3D data by driving condition cuts transmission and processing load while keeping needed scene access.

Braking-profile filtering removes physically impossible surrounding-vehicle maneuvers, cutting trajectory load and collision risk for SDC planning.

Precomputed bend curvature data cuts real-time computation and supports accurate road bend warnings despite variable GPS sampling.

When delays make an event unreachable on time, the vehicle recalculates a route to a later venue window using passenger and operator preferences.

Color-coded in-vehicle light outputs show each passenger trip progress and remaining travel time despite route changes and traffic.

Validated technician commands let stalled autonomous vehicles switch state safely, enabling secure roadside intervention without a driver.

By filtering moving obstacles from road data and combining low-cost sensors with map data, this case improves lane recognition accuracy.

By predicting travel routines and current location, the planner recommends lower-cost charging stops with less station backlog.

A dual-resolution map lets UGVs plan routes broadly while using finer local cells to avoid obstacles with lower processing load.

Predictive remote assistance control slows, reroutes, or stops autonomous vehicles to avoid operator shortages and cut waiting time.

Preprocessing map boundary point sets removes out-of-contour data and restores overlaps, cutting onboard computing demand while improving correction accuracy.

Passerby traffic maps help autonomous robots avoid crowded paths, cutting avoidance actions and travel time to the destination.

Curvature-reduced path generation within a mapped navigable area cuts steering corrections, wear, and speed limits in underground mining.

Preplanned routing uses tunnel maps and boom kinematic limits to keep mining vehicles collision-free in narrow underground passages.

When a planned edge is blocked, a hybrid topological and local map reroutes the mobile robot around obstacles with low computational overhead.

Multiple modems and network maps let a vehicle prioritize data across changing wireless links to avoid congestion and keep remote communication reliable.

Always encoding the merge flag and adding zero-motion candidates keeps video bitstream parsing consistent while reducing error sensitivity.

Fault detection and availability prediction let an autonomous vehicle coordinate nearby service and return with less downtime.

Dispatch logic uses urgency and vehicle risk scores to switch operating modes, balancing ride service with maintenance and fleet redistribution.

Forecasting parking availability from historical and real-time data helps truck drivers use remaining HOS without risking missed parking.

Context-specific HD map indices improve autonomous vehicle localization accuracy while limiting data load and system complexity.

Real-time demand monitoring deploys mobile restrooms, charging stations, or Wi-Fi access where crowds form, balancing public access with business stability.

Switching between network path planning and onboard replanning keeps a mobile device on a safe route when connectivity drops.

Real-time GPS feedback guides haul trucks to target loading positions, improving maneuvering precision and reducing collision risk in mines.

Automatic link correction realigns vehicle sensor data to improve map accuracy and localization while reducing manual error detection effort.

Combining newer autonomous-driving map data with navigation maps reduces road mismatches and visual incongruity during guidance.

Comparing expected and actual anchor-point detection times helps flag positioning errors when GNSS or lidar are unreliable.

Autonomous route selection combines sensor fusion, RFID scanning, and semantic maps to improve security patrols and inventory checks in buildings.

Multispectral visible, infrared, and ultraviolet imaging improves obstacle recognition and navigation reliability when conventional cameras lack contrast.

Merged boundary maps create one operational map, enabling globally shorter paths and fewer redundant planning steps across overlapping work areas.

A 3D virtual map links real-time instrument data to sensor locations, helping operators detect abnormalities and navigate safely in hazardous spaces.

3D imaging, visual odometry, and GNSS fusion keep agricultural vehicles aligned, detect row ends, and turn accurately when GPS is obstructed.

Grid-based latency mapping and repeated path success rates help robots avoid unstable network zones and maintain reliable service coverage.

Always encoding the merge flag and adding zero-motion candidates keeps video parsing independent of candidate-list size and more error resilient.

Onboard RGBD and IMU fusion builds a semantic local map in real time, avoiding pre-mapping and external servers for robot navigation.

Expected anchor-point detection timing is compared with actual detection to catch positioning errors and improve autonomous vehicle reliability.

More frequently updated autonomous driving map data supplements navigation maps to reduce road-environment mismatch and user incongruity.