Anti-Collision Systems TRIZ Cases

Separate costmaps for current and predicted object motion keep mobile-body paths stable while enabling safe stopping or deceleration.

Correlating driver gaze and posture with outward road scenes improves anomaly detection and triggers timely in-cab or remote alerts.

Multiple behavior scenarios for ego and surrounding vehicles improve contact-risk evaluation and route behavior selection in dynamic traffic.

Prediction deviation is checked against detected vehicle position to suppress unnecessary contact avoidance and reduce erroneous intervention.

Discretized critical maneuver trajectories cut message load while preserving vehicle coordination accuracy in dense traffic.

An embedded roadway signal line and onboard field sensors determine lateral vehicle position reliably when cameras fail in poor weather or lighting.

Joint latent scene modeling replaces independent sequential sampling to produce socially consistent multi-actor trajectory forecasts.

An independent secondary controller validates localization, object tracking, and planned paths, then takes over when errors threaten a collision.

Navigation and sensor fusion detect level crossings, warn on unsafe vehicle conditions, and trigger emergency calls if the driver does not react.

Road, vehicle, and environment data are combined to issue context-aware driver warnings with intensity matched to event severity.

Uses gear selection or torque configuration to infer travel direction at very low speed, so parking alerts stay relevant during maneuvers.

Shared vehicle data predicts impaired driver paths and warns or reroutes nearby vehicles to avoid likely collision zones.

Distance-based image segmentation and curve-fitted lane lines improve long-range lane recognition and AR route guidance on curved roads.

Variable-density electromagnetic scanning maps road division lines while cutting irradiation points, processing load, and device scale.

A rotating front camera detects road obstructions hidden by vehicle pillars on curves and alerts the driver with visual or audio warnings.

Speed- and direction-based braking zones curb unnecessary emergency stops while improving obstacle detection accuracy and vehicle safety.

Offline simulation and machine learning update vehicle MPC parameters for changing friction and weather, improving tracking and collision avoidance.

Predicted object paths drive different rear-approach and side-pass alerts, improving vehicle occupant warnings without static speed-only logic.

Consecutive intersection detection lets the vehicle switch driving modes by control difficulty, improving ride continuity and safe handover.

Peeking and overtaking controls are adjusted for intersection obstacles to avoid unnecessary manual takeover and keep automated driving active.

Advance visual, audio, and vibration alerts prompt drivers to release the wheel before autonomous emergency steering, reducing interference.

Activates driver monitoring functions by vehicle situation, conserving limited onboard compute while maintaining dangerous driving detection.

Virtual central, left, and right lane classification improves roadway versus sidewalk recognition so mobile objects can apply the correct speed limit.

When speed drops below a threshold, the controller holds lateral deviation and adjusts deceleration to avoid obstacles with less steering change.

Completing offset control and recentering before lane change smooths automated driving and reduces occupant anxiety from unclear vehicle motion.

Adjusts head-movement drowsiness thresholds by sitting height during vehicle vibration to improve accuracy across different occupants.

Camera-based road characteristic analysis distinguishes multi-lane highways from divided roads to enable lane-centering and lane change assist.

By classifying obstacles as moving or static, the route planner cuts redundant collision calculations and avoids unnecessary slowdowns.

Door-unlock and ignition-based HUD optical path control protects the display panel from sunlight while avoiding unnecessary drive noise.

Real-time trajectory sensing lets an electric trailer follow a towing vehicle virtually, extending range and easing towing loads.

Vehicle-data-triggered lighting and sensors monitor the hitch area to reduce operator-dependent blind-spot risks during low-speed maneuvers.

Rear vehicle hazard assessment uses formation membership and relative position to cut false motorcycle warnings while preserving close group spacing.

Operator-selected trailer target areas help the camera distinguish true trailer features from background objects for more accurate hitch angle tracking.

By estimating whether an aerial object will fall onto the vehicle path, the control logic avoids needless steering and reduces driver discomfort.

Steering data from naturally high-load road sections enables objective driving ability evaluation without disrupting normal driving.

Potential collision areas based on vehicle and object speed suppress false safety activation during right and left turns.

Blind-region detection is split by contact risk so vehicles can use faster recognition near collision paths and more reliable processing farther away.

Secondary target detection on the avoidance steering route suppresses automatic steering and lowers collision risk beyond the primary target.

During lane changes, the distance indicator is hidden to avoid crossing lane markings and reduce occupant discomfort.

Sensor-detected flaggers trigger a threshold stop and teleoperator guidance, helping autonomous vehicles pass safely and follow traffic directions.

Blind-spot detection is paired with rider-state-based steering intervention to avoid nearby vehicles without sudden motorcycle behavior changes.

Optical braking signals let each autonomous vehicle set its own deceleration from spacing and upstream braking, improving comfort and flow.

Biometric data is matched to driving, non-driving, and transition states to estimate accident risk more accurately across changing driver conditions.

Dynamic safety envelopes use real-time detection and rule switching to monitor nearby objects, prevent accidents, and meet travel limits.

Vehicle-to-vehicle coordination assigns virtual traffic lights at unsignalized intersections to manage crossing order and improve traffic flow safely.

A two-stage TTC prediction flow refines collision state in oblique crossings to improve risk evaluation and braking control.

Visual and auditory alerts intensify when a destination is set, helping passengers notice autonomous driving start readiness without excess annoyance.

A target-lane detection zone blocks autonomous lane changes when a fast-approaching vehicle may enter, reducing sudden intrusion risk.

Message-based digital twins replace real-time video in teleoperated driving to cut bandwidth, reduce latency, and improve link robustness.