Autonomous Decision Making Process TRIZ Cases

A controller matches stranded EVs with nearby vehicles or charging locations using SOC, location, route, and availability data.

By reducing active planning dimensions over time, this case extends autonomous vehicle trajectory horizons without prohibitive computation.

Sensor-equipped mobile robots detect walking hazards along mapped routes and reroute or alert staff to reduce pedestrian injuries in retail spaces.

Segmented data packets let users swap perception, planning, and control modules to speed autonomous driving algorithm verification.

Path polygons and agent trajectories predict collision zones and cut unnecessary vehicle yield while maintaining reliable avoidance.

Surface matching across consecutive sensor spins corrects motion distortion and segmentation errors for more accurate autonomous vehicle tracking.

Confidence-scored parking layout models classify gaps earlier, cutting parking latency and enabling smoother vehicle trajectories.

Track-based rail agent prediction filters likely paths and motion plans so autonomous vehicles can avoid conflicts with smoother, lower-overhead navigation.

Real-time passenger location and intent let a driverless vehicle attempt safe fly-by pickups before reaching the preset pickup area.

A passenger identifier lets an autonomous vehicle verify the rider and refine pickup location when phone battery, service, or crowding blocks app-based hailing.

Obstacle motion, speed, and acceleration are converted into repulsion ring coefficients to plan smoother, safer autonomous driving paths.

Co-located camera modules deliver seamless 360° vehicle sensing while shielding EMI and managing heat to preserve image alignment.

Adjustable optical filtering and sensor-quality checks help vehicles maintain safe cruise control when direct sunlight degrades camera data.

A safety time horizon lets an autonomous vehicle wait for runtime exceptions to self-resolve before unnecessary precautionary maneuvers.

Magnetic modular UUV assemblies let operators swap propulsion, control, and sensor modules in the field to cut cost and expand mission flexibility.

Authenticated external triggers let autonomous vehicles respond to emergency commands without sensor false positives or malicious takeover.

Operator and environmental risk profiles guide autonomy changes and alerts, improving control handover when drivers may be inattentive.

Bidirectional sensor-to-processor feedback corrects abstract data mismatches to improve recognition accuracy for autonomous driving decisions.

Driving style patterns and occupant feedback are shared with nearby vehicles to warn drivers and adapt responses to unpredictable AV behavior.

Integrated lidar and camera placement reduces occlusion and blind spots, improving close-range object classification for autonomous driving.

Real-time V2X messages share stopping distance, acceleration, turning radius, and autonomy status to improve spacing and maneuver decisions.

Continuous PIR, heat, and wireless sensing identifies likely vehicle attacks early, enabling staged countermeasures before damage occurs.

Redundant vehicle computers vote on autonomous driving commands while a command controller checks memory integrity and blocks faulty outputs.

Autonomous drones enter flooded buildings, detect high-moisture zones, and vacuum water and mud to limit structural damage without human entry.

Predicting tire-ground friction ahead of the vehicle lets autonomous control adjust speed and safety distance before low-grip conditions are reached.

Environmental monitoring adjusts autonomous vehicle sensor field of view in real time to maintain detection confidence and navigation reliability.

Virtual and closed-track scenarios score autonomous driving behavior from performance data, enabling insurance premium and deductible assessment.

Driver input amount and duration trigger automatic shifts between autonomous, cooperative, and manual driving to reduce switch burden.

A refined 3D vehicle model filters self-returns and transient obstructions, improving sensor interpretation for driving decisions.

Vehicle cameras and AI detect danger, trigger sirens, alert emergency services, and control movement to assist distressed people.

Predicted ride demand is used to time minimum autonomous vehicle servicing in low-demand windows, preserving fleet availability during peaks.

Distributed training across vehicle and roadside units cuts onboard sensor and compute burden while improving trip planning and ADS coordination.

Sensor-guided mobile robots map routes, detect walking hazards, and trigger rerouting or alerts to reduce pedestrian injuries in retail spaces.

Pre-authenticating an assigned passenger and checking nearby pedestrian distance lets an autonomous vehicle stop early for safe boarding.

Cloud-based roadside resources supplement onboard sensing and control to maintain CAV autonomy in abnormal driving while reducing power demand.

Vector subtraction removes vehicle motion from onboard wind measurements, giving accurate stationary wind speed and direction for safer driving.

Route landmarks and fleet coordination let trucks recalibrate sensors in motion, avoiding downtime while maintaining calibration accuracy.

Mapped traffic light regions and configuration-specific AI improve state recognition accuracy for more reliable autonomous vehicle control.

When disasters block access to priority refuge floors, the controller reroutes the vehicle to a safe same-floor evacuation point for faster rescue.

Rasterized path and obstacle inputs help a DNN assign obstacles to paths more accurately while avoiding slow 3D-to-2D projection steps.

In-cabin sensing and adaptive alerts help drivers re-engage manual control when autonomous capability drops under adverse conditions.

Fusion tracking IDs replace heavier Kalman-based processing to cut delay and computing load while keeping vehicle target tracking accurate.

Backward filtering turns stored production sensor data into ground truth, flagging ADAS perception deviations without massive cloud uploads.

Driving-mode-based path generation matches turning radius to two- or four-wheel travel, reducing headlands and improving automatic work efficiency.

Feedback from a processing unit lets autonomous driving sensors refine recognition algorithms and reduce mismatch with real traffic scenes.

Distributed value iteration lets autonomous vehicles share local conditions and update routes in real time, easing centralized routing complexity.

Surface matching across consecutive sensor spins corrects motion distortion and segmentation errors for more accurate autonomous vehicle object tracking.

Image-based driver state monitoring identifies unfit operators and changes autonomous mode in real time to reduce hazardous driving situations.

Predicted input data keeps autonomous driving models operating through data errors by adjusting thresholds and confidence for driving decisions.