External Condition Input Parameters TRIZ Cases

Real-time driving features and style probabilities let automated driving adjust acceleration, deceleration, and lane-change timing to driver habits.

Vehicle-state mapping gives wheel rolling radius estimation a closer starting point, cutting convergence time for heavy-duty vehicle motion control.

Personalized driver behavior models and trajectory prediction adapt warning intensity to improve collision avoidance and driver reaction time.

Road gradient and vehicle speed are used to stop coasting or power-running control before speed or deceleration harms energy-efficient driving.

Criticality-based message ranking helps an ego vehicle accept urgent connected-vehicle maneuvers first, reducing conflicts in cooperative driving.

Real-time driver skill estimation adjusts steering and braking support on curved roads, avoiding over-assistance while aiding less skilled drivers.

When road structure changes after same-direction lane detection, this control logic blocks unsafe lane shifts that could cause reverse travel.

Fused optical, ultrasonic, and radar sensing predicts low-speed impact threats so parked vehicles can move autonomously to avoid collisions.

Limits acceleration when turn intent and high steering speed are detected, then restores response as steering input settles.

A higher-order fast-time and slow-time signal model cuts range and range-rate errors in constant-PRP stepped-frequency radar.

Pedestrian signal countdown time is used to judge intersection passage feasibility and adjust vehicle speed before the light changes.

Road and driver-operation data are combined to trigger deceleration only when the driver intends to enter a branch lane needing speed reduction.

Pre-generated action trajectories feed vehicle tree search, improving collision prediction accuracy without adding real-time planning delay.

Matches teleoperated driving sessions to control centers using control type, latency, and capability requirements for safer allocation.

Environment sensing predicts road and wind effects so motion support devices can pre-compensate, improving heavy-duty vehicle stability and reducing wear.

Collision-risk feedback limits acceleration after accelerator override, helping the vehicle avoid unintended forward surges near obstacles.

Switching from camera lane markings to map-based lane width during lane changes helps avoid false recognition and keeps autonomous vehicles stable.

Intersection complexity from signal and map data switches automated longitudinal guidance between automatic and user-confirmed modes.

A receding-horizon controller uses route data and lead-vehicle speed to cut energy use while handling stop, resume, and follow decisions.

Road-gradient prediction helps driver assistance adjust speed and lane-change timing for safer exits in dense multi-lane traffic.

Asynchronous trajectory queues let perception and collision checks cut latency and redundant processing in dynamic vehicle safety control.

A motion-based safety region is checked against a safe driving corridor to trigger timely maneuvers around static and dynamic obstacles.

Real-time sensor and driving performance verification adjusts autonomous driving control parameters to handle sensor degradation and vehicle differences.

A safety verification layer filters delayed or out-of-order fleet commands using perception and vehicle status data to keep connected vehicles stable.

Displays distance or time to two lane-change points so drivers can judge when automatic assistance ends and take over safely.

Machine learning and dynamic programming split cloud and vehicle tasks to cut energy use while maintaining stable driving under traffic and road conditions.

Fusing aerial map imagery with radar and onboard sensing improves vehicle location estimates for more precise driving control.

Risk candidate scene extraction lets occupants review ML driving inputs and commands, improving autonomous control monitoring with less data load.

Maintains a minimum following distance so onboard sensors can keep traffic lights visible and avoid abrupt braking at junctions.

Sensor and context analysis identify the safest left-turn waiting spot, balancing collision risk with space for passing traffic.

By adding heading angle, lateral position, and turning thresholds, avoidance control avoids unnecessary ADAS intervention while preserving collision accuracy.

Dynamic onboard-to-cloud task allocation cuts battery drain from autonomous computing while preserving real-time vehicle response.

Factor-based acceleration and deceleration limits help maintain following distance while avoiding abrupt speed changes that unsettle drivers and rear vehicles.

When a motorcycle-like lean vehicle nears a reference speed, control switches from gap-based cruise to speed holding to prevent instability and falls.

Path-history divergence tracking helps automated vehicles hold or drop the closest-in-path target to smooth braking and acceleration.

A dedicated lane change button replaces complex turn-signal actions, enabling easier assisted or automatic lane changes from driver input.

Camera and radar or lidar data feed a machine-learned model that flags risky nearby vehicles early and alerts the driver or vehicle controls.

Driving-environment filtering narrows FCA target objects by road type and sensor cross-checks to cut computation and avoid misrecognition.

Measures driver reaction to recognized road events to diagnose health states more accurately than biometrics alone and trigger alerts.

Acceleration sensor patterns from braille blocks let a mobile object distinguish sidewalk from roadway when peripheral images are unavailable.

A cloud server matches vehicle capabilities and planned routes to form platoons more flexibly across varied driving scenarios and traffic conditions.

Probabilistic path prediction and suggested actuator inputs help drivers optimize lap performance and respond faster to emergency events.

A four-subsystem master-slave ring keeps autonomous vehicle control commands available during faults, enabling safe degraded driving or standstill.

A return-lane likelihood check lets vehicle guidance switch or drop the target object after an aborted lane change to avoid unsafe spacing.

Adjusts following distance near crossings so a stopped vehicle leaves room for turning traffic and avoids stop-line interference.

Variable time-gap ACC adjusts vehicle speed in real time during following and cornering to improve response to front-vehicle speed changes.

Predicts a driver's future path against a personalized travel envelope, intervening only when behavior exceeds individual limits.

A vehicle control unit switches between RTK-enhanced and nominal ODDs to keep motion control precise when correction data is available and safe when it is not.

LiDAR-based ego-lane tracking identifies first and second closest preceding vehicles, preserving accurate front-object perception during occlusion.

A decoupling estimator and pretrained ML model predict smoother ACC speed profiles around traffic events to improve fuel economy and comfort.

Multiple candidate object positions are clustered by behavior to improve autonomous vehicle planning accuracy while limiting computation.

Context-aware trigger detection prompts drivers about available vehicle functions and enables quick activation or training in suitable situations.

Direct and indirect environment data are combined into a risk score to enter, adjust, or cancel remote parking before collisions occur.

Multi-sensor driver monitoring links fatigue, sentiment, and abnormal behavior detection to warnings, speed limiting, deceleration, and braking.

Real-time sensor, GPS, and remote-computing guidance helps drivers back a trailer accurately with live hitch views and adaptive on-screen cues.

Upcoming route incidents are prioritized into one continuous speed profile, reducing abrupt deceleration changes and improving driving comfort.

Multi-stage gap search, safety evaluation, and deep reinforcement learning improve autonomous lane changes without sacrificing safety.

Nearby vehicle speed and distance sensing helps adaptive cruise control react faster than lead-only systems and maintain safer separation.

Graph search narrows object trajectories to relevant road segments, improving vehicle action prediction speed, accuracy, and control safety.

Interior and temperature sensors detect occupant emotion and preferences, letting automated driving adapt style and cabin settings for comfort.

Multi-vehicle V2X data helps verify whether abnormal driving originates from the ego vehicle or another vehicle, reducing sensor-only misjudgment.

Blending inertial and running slope data with INS and GNSS removes elevation drift, enabling accurate road profiles at stops and low speeds.

Traffic situations are decomposed into hierarchical partial situations to cut planning computation while preserving safety guarantees.

Slippery-road detection lets one-pedal drive reduce deceleration in advance, improving EV stability while preserving energy recovery.

Manages overlapping torque requests by holding the first target to its limit, then switching control to the second without time lag.

User-specific deceleration and indicator control at roundabouts improves autonomous driving comfort and stability while reducing manual takeover.

Real-time V2X alerts on micromobility count, speed, and direction help vehicles avoid collisions in crowded areas.

Held speed suppression based on trajectory deviation prevents re-deviation and repeated acceleration while restoring comfort after curve recovery.

Intermediate-feature density modeling with a GMM flags unseen objects and calibrates classifier confidence for safer autonomous driving.

Multiple camera and radar indices predict adjacent-lane cut-ins early, allowing braking or steering timing to be adjusted to avoid collisions.

On-board cameras and map data let cruise control detect traffic signals and apply phased deceleration without costly wireless infrastructure.

Separating actual and potential road risk points into calibrated reaction models improves autonomous driving evaluation accuracy and safety.

Real-time vehicle and environment data drive changing control profiles to reduce monotony and improve driver hazard alerting.

Slippery road detection lets one-pedal drive reduce or switch deceleration modes before pedal release to maintain vehicle stability.

Driver monitoring, rear radar, and passenger release logic help an emergency stop maneuver find a safer stopping position when adjacent lanes are partly obscured.

Switches between delayed and anticipatory braking based on adjacent-lane detection to avoid premature slowing behind slow vehicles.

Rear-vehicle radar and lidar data predict collision risk early, enabling automatic speed or steering escape maneuvers before driver reaction delays.

Independent channels cross-check vehicle trajectories with different environment models and safety metrics to meet ASIL D at SAE Level 3+.

Driving parameters for target speed, spacing, tolerances, and ramp values let a platoon adapt density while maintaining safety and efficiency.

Time-to-contact scoring combines lateral and longitudinal metrics to flag unsafe autonomous lane changes before execution.

Multi-sensor verification activates safety assist when driving assist is requested, reducing false triggers from low-accuracy sensors.

Threshold-based speed change proposals suppress unnecessary cruise speed prompts while preserving driver control during constant speed travel.

When GPS reception is weak, synchronized sound pressure and position data estimate vehicle speed with lower processing load for noise diagnosis.

A relevance indicator keeps speed control aligned with a lost target vehicle, reducing abrupt acceleration or braking when detection returns.

Real-time vehicle and environmental data update multisensory control profiles to reduce monotonous mode use and keep drivers engaged.

Crosswalk costs from sensor and map heatmaps help autonomous vehicles slow, stop, or proceed safely around pedestrians.

When autonomous driving fails, the vehicle selects an MRM stop strategy from sensor and state data to minimize nearby collision risk.

Fleet-derived route data is checked against onboard sensing to keep lateral guidance reliable when lane detection is degraded or occluded.

Detects side-shown stopped vehicles and adapts warning, steering, and braking to avoid false control and prevent collisions.

Uses self-location, turning, and steering data to detect vehicle or cargo center of gravity without added height sensors.

Tracks objects through occluded regions by propagating uncertainty, then adjusts the vehicle path to reduce collision risk.

A controller evaluates nearby traffic, moves the vehicle to a safe lane or stop, and then hands over from autonomous to manual driving.

Real-time pedal input and road type are matched to reference driver behavior to adjust torque for smoother, more adaptive acceleration.

A controller checks traffic, selects a reachable safe lane or stop location, and confirms driver consent before manual takeover.

Contour-based corner overlap analysis generates more accurate vehicle path bounds in large-curvature turns, reducing planning error and collision risk.

A staged far-, mid-, and near-field gap search cuts lane-change computing load while extending awareness beyond sensor range.

Intersection signal data lets a lead vehicle split forward and following platoons so vehicles handle dilemma zones safely and rejoin later.

A dual-controller vehicle motion scheme switches from nominal driving to evasive control to react quickly to unexpected events while keeping the vehicle in a safe region.

In-cabin sensing tracks driver readiness and occupant health so a Level 3 vehicle can continue automated driving and trigger emergency action.

Sensor-based vehicle polygons help predict parallel parking intent, allowing autonomous vehicles to adjust path and spacing safely.

When wheel float is detected, steering angle hold prevents unintended yaw-related steering changes and helps stabilize the vehicle on uneven terrain.

Road-profile sensing predicts upcoming curves and reduces speed early, avoiding abrupt braking, occupant discomfort, and component wear.

Road sign and geolocation fusion refines roundabout entry distance, enabling smoother autonomous speed regulation with simple maps.

Small-angle trajectory approximation calculates vehicle-object lateral offset without square roots, cutting MCU computation time and power.

Navigation preview and observed lead-vehicle behavior shape ego speed setpoints to cut fuel use and emissions while maintaining following constraints.

Precomputed lateral and longitudinal trajectories cut real-time computation, enabling faster obstacle avoidance with synchronized braking and steering.

Vertical acceleration, tire stiffness, and damping data are used to estimate road IRI more frequently and at lower cost than profilometer surveys.

Switching from road-map guidance to destination premises routing helps cargo vehicles reach the correct stop position for handling.

Independent mechanical and electrical braking stop an errant autonomous vehicle remotely, even if onboard controls fail.

Nearby obstacles are grouped into one target so AES can stay active and plan avoidance paths even when objects span lanes.

Dynamic protection zones around an unmanned escort vehicle keep manned vehicles on safe work site paths while allowing driver deviation when needed.

Acceleration and deceleration cues warn inattentive occupants of likely automation disengagement, cutting reaction time and improving awareness.

Multi-step conditional policies and forward simulation improve autonomous driving decisions in complex scenarios while reducing disruption and compute load.

Situation-based display modes adapt driving guidance for narrow roads, intersections, and merging while adding steering prompts near obstacles.

Automatically records vehicle trajectories, checks user awareness, and issues degradation alerts only when needed to save storage and avoid noise.

Map-based lane counting switches ramp target selection logic, improving single-lane vehicle tracking without prediction or curvature calculations.

Sensor fusion from cameras and radar enables Level 3 vehicles to time passing lane changes and speed control to avoid rearward collision risks.

When autonomous driving cannot continue without occupants, limited authority can shift to a designated second user under policy constraints.

When a signal changes after stopping at an intersection, projection-based restart control helps automated vehicles clear the junction safely.

When convoy vehicles face obstacles they cannot all traverse, following mode is selectively disengaged so each vehicle can avoid safely and rejoin later.

Predicting lead-vehicle movement at traffic lights enables early clutch and torque engagement, cutting autonomous vehicle start delay.