Anti-Collision Systems TRIZ Cases

Sensor-based avoidance scores help autonomous trucks update lane plans near on-ramp merges to reduce trajectory conflicts and discomfort.

When lane change assist is canceled, the system alerts nearby vehicles with indicator, sound, and V2V signals to avoid confusion and unnecessary braking.

Two trained models predict whether a second vehicle will yield during merging by comparing time-series parameter distributions.

Dual sensor and power subsystems keep longitudinal and lateral vehicle control active when one control path fails.

An overhead image shown in the electronic side mirror and front display helps drivers spot low obstacles and ditches without losing forward attention.

A vehicle-mounted display shows failure and behavior information to following drivers, improving hazard understanding and response.

Real-time camera and ECU alerts help drivers judge lateral distance and speed when overtaking bicycles under local traffic rules.

A lean vehicle support control adjusts its reference region from travel locus, road width, and lean angle to guide safe obstacle positioning.

Secondary perception models fuse multi-sensor occupancy maps to validate or replace unsafe vehicle trajectories while limiting compute load.

By checking whether nearby vehicles can slow before a merge point, this case enables coordinated deceleration for safer, smoother merging.

Relative-speed-based acceleration control helps a vehicle pass out-of-lane objects smoothly while reducing passenger uneasiness and brake override.

Adaptive control of alert timing, content, and modality helps drivers recognize warnings clearly without losing trust as alert volume grows.

Context-aware voice alerts name detected road users and switch between direct or indirect warnings to reduce driver confusion.

Radar-based rear warning is stabilized by comparing target vehicle length over time to correct shifting tracking points and avoid premature alerts loss.

A sensor-equipped lead AGV guides simpler follower vehicles, cutting onboard sensing and computing while maintaining platoon safety.

A vehicle control approach anticipates intersecting road user movements and adjusts its path to reduce collision risk and traffic delay.

Predicting the vehicle path through intersections with lane, road link, and obstacle data cuts false risk alerts without high-precision maps.

When lane change assistance judges a maneuver difficult, occupant feedback helps continue or cancel attempts to reduce fatigue and wasted operations.

Real-time braking, steering, and acceleration sequences help avoid imminent crashes or reduce impact while adapting to driver skill and road conditions.

Prepares collision avoidance earlier during left and right turns by assessing fast blind-spot traffic missed by surrounding sensors.

Advance alerts before control-state shifts and following-distance changes help drivers stay aware and comfortable during automated driving.

Multiple rear ultrasonic sensors widen the high-speed reverse brake zone to cover displaced obstacle positions and prevent collisions.

Rear obstacle alerts shift or extend beyond the bumper when an open tailgate or cargo protrudes, helping drivers avoid rear collisions.

Repeated traffic signal recognition improves intersection collision warnings and vehicle control by validating signal state before risk assessment.

Situation-based display timing and content make autonomous-to-driver handover alerts clearer during traffic congestion driving.

Polynomial reference paths, Stanley control, and kinematic fitting create collision-free trajectories that non-holonomic vehicles can actually follow.

Physics-based safety envelopes use situation-specific rules to monitor moving-object violations and improve host vehicle driving safety.

Parallel subnetworks fuse vehicle state, interaction, and road data to predict lane changes more accurately for safer autonomous route updates.

Rotation-direction checks replace equation solving to detect vehicle boundary crossings with lower computation and more reliable results.

Color-segmented route images show which autonomous driving sections have passed safety checks and which still need verification.

Independent trajectory checks and component monitoring help autonomous vehicles catch pipeline errors and trigger safer collision-avoidance maneuvers.

Onboard sensing tracks lane position, passing events, and travel distance to alert slow-lane misuse and prompt a lane change.

Stored periphery sensor maps let the vehicle match similar surroundings and trigger the same driving assistance in comparable hazard locations.

Blended insertion, curve, and headway velocity profiles smooth lane changes within a speed-based time window for better ride comfort.

By tracking distance changes to lane markings, the recorder warns of lane departure accurately despite different mounting positions.

Collision risk prediction and lane departure intent help trigger lane keeping only when needed, reducing unsafe interventions in complex traffic.

Advance HMI action prompts help drivers regain awareness and prepare for control transfer when high-level automated driving ends.

Continuous comparison of parked-vehicle surroundings detects new hazards and warns drivers when blind spots or remote parking reduce awareness.

By shaping longitudinal and lateral acceleration into one composite peak, this case reduces vehicle vibration and unstable behavior.

Predictive blind-spot object estimation and risk maps help avoid sudden deceleration while suppressing unnecessary lane changes.

Combining sensor inputs with planned paths from driver assistance functions improves long-horizon vehicle trajectory prediction and collision avoidance.

Differentiated suspension conditions let autonomous lane changes continue toward a set destination while remaining easier to cancel in other scenes.

Receiving FCA operation signals from preceding vehicles lets an autonomous vehicle restrict acceleration and adjust TTC to prevent serial collisions.

Predicts collision probability in narrow passing areas and adjusts vehicle trajectories to avoid crashes without lane changes.

Relative speed, blind-area position, and elapsed time are combined to avoid unnecessary vehicle speed adjustments and improve driver comfort.

Integrated virtual risks smooth blind spot vehicle control by combining latent and apparent hazards to avoid abrupt speed and steering changes.

When following control relies on a low-accuracy long-range sensor, control changes are softened to avoid abrupt speed shifts and occupant discomfort.

A single control input lets the vehicle preselect an adjacent or distant lane and complete multi-lane lane changes without repeated operation.

Vehicle sensors estimate lane cant angle to correct road-shape image placement, reducing camera misalignment without high-definition maps.

Time-based stop-hold control keeps a vehicle stopped near pedestrians on narrow roads until safe separation is restored.

Projects path-based driving risk onto the real scene by scoring nearby objects and highlighting predicted high-risk movements in AR.

Forward simulations rank collision, confinement, and comfort risks so autonomous vehicles avoid unnecessary braking while staying safe.

Dead-reckoning distance tracking keeps platooning stable on slopes when front sensors lose sight of the preceding vehicle.

Blocked-area ratio control suppresses unnecessary A-pillar transparent-view images, reducing driver discomfort, energy use, and display wear.

Staged hands-off warnings and immediate high-level alerts help lane following assist respond when steering input is absent or conditions become unsafe.

Predicts collisions between nearby and remote surrounding vehicles so the ego-vehicle can warn the driver before external crash risks escalate.

Yaw-responsive cameras expand blind-spot coverage during turns, warning drivers of pedestrians or cyclists and helping avoid collisions.

Repeatedly predicting how surrounding objects influence each other extends vehicle behavior forecasting beyond short-term limits while keeping accuracy high.

Controlled trailer pivoting creates extra braking space during platoon emergency stops, reducing rear-collision risk and chain impacts.

Stopping the host vehicle early to reserve admission distance lets a target vehicle enter safely without creating new collision risk.

Correlating roadside object detection with ego-vehicle behavior changes creates scalable lane labels for accurate multi-lane autonomous driving.

Transition cues are timed by driving state so drivers can recognize when hands-off and specific-action permissions become allowable or active.

Driver inputs and behavior are converted into V2X intent messages, helping nearby vehicles predict maneuvers and avoid dead-zone collisions.

Fused ego and road-user occupancy maps improve long-horizon collision prediction in complex traffic while reducing false ADAS warnings.

Overlapping sensor views trigger synchronization only when object positions diverge, cutting processing load while maintaining recognition accuracy.

Lateral obstacle detection helps predict when a pedestrian may be pushed into the lane, enabling earlier steering assistance with fewer unnecessary interventions.

Camera and radar data estimate approaching vehicle arrival time to judge turn safety and trigger warnings or braking at intersections.

Uses lateral overlap change rate to distinguish diagonal lane changes from true rear threats and suppress unnecessary driver warnings.

Filters reflected rear cross-traffic detections using object position, detection time, and intervening stationary objects to suppress false warnings.

Comparing current and prior detection cycles helps blind spot warnings distinguish moving vehicles from stationary objects and avoid false alerts.

Timed driver confirmation and progress-based speed control help execute lane changes safely or cancel them before surrounding traffic is affected.

Warnings are triggered from trajectory deviation and the latest evasion point, cutting false alerts from adjacent-lane objects.

Sensor-based alert levels distinguish visible from blind spot obstacles, reducing driver distraction while preserving collision warnings.

Hands-on and start notifications keep drivers gripping the wheel until autonomous travel begins, reducing transition confusion and control errors.

Early brake-light, warning-light, and horn alerts give following drivers time to react before AEB braking starts.

Natural-phenomenon-aware risk assessment adjusts vehicle actuator control for weather and backlight to cut collision risk without annoying drivers.

Location-based warning and braking control lets forward collision avoidance react earlier to pedestrians in children protection zones.

When failures or path deviations occur, the MRM system steers, slows, or changes lanes to reduce risk and resume autonomous driving safely.

False obstacle detections can block valid parking; this case uses 2D and 3D sensing plus occupant correction to restore route calculation.

Route timing is adjusted with delays or speedups so vehicles do not need remote assistance at the same time, cutting teleoperator demand.

Video-based models classify riders and micro-mobility vehicles together to predict actions more accurately for autonomous driving alerts.

Machine learning tracks adjacent-lane yaw and lateral motion to detect truncated cut-in vehicles earlier and trigger faster alerts or braking.

Driver gaze tracking helps ADAS judge obstacle awareness, lowering false alarms while triggering faster responses when attention is lacking.

When hands-off lane keeping exceeds the road speed limit, the controller shifts to hands-on mode to restore driver engagement.

Aggregated vehicle and onboard sensor data trigger timed driver takeover alerts when autonomous driving faces complex road conditions.

A dielectric protective cover keeps water away from the RFID antenna so road magnetic markers maintain stable wireless communication in rain.