External Condition Input Parameters TRIZ Cases

Multiple object-based controllers and gain scheduling combine acceleration commands to smooth autonomous driving and improve safety.

Passing distance feedback adjusts steering assist to match driver behavior, improving safe clearance from oncoming vehicles.

Driver speed changes are learned by road type to update set-speed corrections, reducing repeated intervention on straight and curved roads.

Computes the highest pre-limit vehicle speed from distance and deceleration bounds to avoid unnecessary braking and reduce energy loss.

Calculating convergent transition distance lets autonomous vehicles switch paths smoothly, improving stability and passenger comfort.

Behavior-based mission models synchronize multiple autonomous agents, enabling contingency handling in dynamic and crowded environments.

LiDAR reflectance, distance, and return counts are combined into heat maps to distinguish dry, wet, snowy, slushy, and icy roads in real time.

Upper and lower range radars estimate obstacle height with simpler processing than laser radar, helping buses avoid height-based blind spots.

Reflective sensor returns can distort roadway ground profiles; this case removes outlier points and re-estimates the surface for safer trajectory planning.

Real-time sensor and map cross-checking flags likely adversarial regions and adapts vehicle motion to maintain reliable autonomous driving.

RF reflections reveal surface moisture and friction so vehicle controls can adjust tire force and speed to maintain traction on wet or dry soil.

Risk and driving-ability scoring adjusts stability and entry-avoidance thresholds to keep drivers safe without eroding driving skills.

Deep learning estimates off-road surface severity from travel data, helping predict stuck risk and switch vehicle modes earlier.

Collision notifications let nearby autonomous vehicles assess braking distance or change lanes early to avoid multi-vehicle pile-ups.

Predicting a preceding vehicle's acceleration, braking, and speed limits improves overtaking recommendations on upcoming road segments.

Obstacle detection steers the delivery robot obliquely across grooves and orthogonally over steps to reduce wheel derailment.

Fusing sensor kinematics with planner semantics improves prediction of lane changes or braking, enabling more precise automated driving control.

An unmanned vehicle is selected and routed to obstruct a danger driver while reducing human risk and limiting vehicle damage.

Intermediate intention signals help autonomous vehicles predict nearby agent behavior with lower latency and less sensor-processing load.

Real-time route GUI compares actual and estimated vehicle speed on an energy-efficient track to guide driving and monitor energy use.

Learns driver risk perception and combines it with obstacle risk maps to set autonomous driving conditions that reduce occupant anxiety.

Dynamic gain changes based on predicted map-position correction help autonomous vehicles track target paths through lane changes without unexpected behavior.

Swarm-based trajectory shifting keeps a preset distance from road boundaries, helping automated vehicles pass safely on narrow single-lane roads.

Position-based speed adjustment lets user and delivery vehicles meet while moving, cutting wait time and improving handoff reliability.

Road-ahead signals let the suspension adjust before impacts, keeping loads below limits to reduce wear, failures, and ride discomfort.

A recommendation module assigns steering, braking, and acceleration between driver and automation to improve mixed-mode driving safety and accuracy.

Real-time visibility sensing lets an autonomous vehicle adjust speed and following distance in fog, tunnels, night driving, and canyons.

Acceleration is temporarily limited when a vehicle starts from rest, giving sensors and overrides more time to avoid pedestrian collisions.

Optimized axle force trajectories cut multi-trailer off-tracking during abrupt maneuvers while keeping wheel slip within safe limits.

Discrete object trajectory checks let autonomous vehicles trigger timely emergency maneuvers without excessive computation or overly cautious driving.

Frequency-based blending of yaw and steering angle signals cuts noise and delay, reducing steering oscillation on slippery or uneven roads.

Surrounding vehicle position and speed are used to correct driver gaze estimation and improve side mirror watching detection.

Correlating radar and lidar returns helps detect fog density, filter particle reflections, and preserve reliable vehicle distance sensing.

A switchable retarder and polarizing beam splitter steer LiDAR beams by polarization to widen field of view with fewer units.

Radar backscatter and rain-rate models let vehicles estimate rainfall in real time, adjust sensing and routing, and share local weather data.

Variable deceleration near curve entrances uses driver brake input to stop support control when manual deceleration is strong enough.

Curve-speed support decelerates toward a target speed, then stops when accelerator input rises, preserving driver intent on curved roads.

Switching between narrow and wide speed-control ranges helps maintain set speed, improve energy use, and preserve safe vehicle spacing.

Detects ice, snow, and temperature risk, then shifts axle positions to a second parking spot before tires form trapping icy hollows.

Switching between narrow and wide speed ranges lets cruise assistance balance speed stability, energy use, and safe inter-vehicle spacing.

A trapezoidal longitudinal acceleration profile limits jerk during gear changes and adapts to vehicle and target behavior for smoother ACC driving.

A dual-domain vehicle system uses a primary display path and an independent backup channel to ensure reliable driver takeover requests.

A GNN combines object interactions with map boundaries to correct off-road predictions and keep autonomous vehicle trajectories realistic.

A lower cut-in threshold when stopped lets the front camera detect entering vehicles earlier and reduce sudden braking risk.

Real-time axle load and wheel speed sensing switches 2WD and 4WD modes to cut tire wear, running resistance, and fuel use.

Adjusts entry prohibition areas by lane-change progress so vehicles avoid destination-lane obstacles without unnatural returns.

Corrected lane paths increase turning radius for commercial vehicles, helping prevent curb or lane-boundary collisions in tight turns.

Predicting braking torque from PTO load lets cruise control plan speed changes ahead, cutting unnecessary energy use and operating cost.

Historic brake actuation data guides following distance selection to balance propulsion energy savings against braking losses and discomfort.