Vehicle Condition Input Parameters TRIZ Cases

Trailer-mounted radar extends rear field of view blocked by a towed trailer, reducing interference and improving object detection for driver assistance.

Temporary excitation acceleration updates vehicle mass and handling estimates from sensor data, helping ADAS adapt to changing conditions.

Using level-sensor motion and a suspension-damping model, this case estimates vehicle payload mass more accurately during movement.

Driver trajectory prediction and risk evaluation shift control weight between human and automation to preserve safe intervention in intelligent vehicles.

A self-supervised RNN replaces manual MPC tuning by adapting vehicle control to unknown road friction, tire wear, and new actuators.

By predicting actuator limits and converting them into acceleration bounds, the planner avoids non-executable trajectories and controller windup.

Sensor-driven steering and braking coordination counters trailer sway by adjusting yaw and deceleration to reduce towing control effort.

Adaptive accelerator thresholds detect towing and avoid unnecessary drive force limits during vehicle startup, including slope and obstacle conditions.

Closed-loop rear-axle torque vectoring corrects wheel slip and yaw deviation during cornering to neutralize oversteer and understeer.

Camera and radar data identify a coupled trailer so FCA braking and warning timing can be adjusted to prevent jackknife and swing.

A physics-based control node converts planned acceleration into virtual pedal, torque, and brake signals, reducing vehicle tuning effort.

Passenger location sensing lets vehicle control limit acceleration and jerk at occupied seats, improving comfort and stability during turns.

Dynamic wheel slip angle limits adapt to target acceleration and curvature, improving heavy-duty vehicle stability without overly restricting control freedom.

Tire grip classes are used to rank trucks and set following gaps, improving platoon braking safety and drag reduction.

Bayes filter state estimation replaces convergence calculation to generate stable vehicle trajectories and speeds under state constraints.

Real-time sensor fusion and model predictive control coordinate chassis and driveline actuators to maximize tire force and maintain vehicle stability.

Driving path prediction adapts to lift axle, steerable axle, and load state to improve obstacle detection and cornering accuracy.

Real-time wheel and vehicle speed feedback limits axle torque when slip rises, preserving traction and controllability during acceleration and cornering.

A motorized rooftop sensor retracts before low-clearance zones and redeploys after passage, expanding autonomous vehicle route coverage.

LiDAR point clouds define trailer planes and orientation in real time, helping autonomous port vehicles avoid collisions during maneuvering.

Earlier lane change initiation when towing helps autonomous vehicles merge more smoothly and reduce pressure on surrounding traffic.

Preplanned turn execution points and section-specific vehicle posture help autonomous delivery vehicles pass narrow routes and avoid rollovers.

Thermomechanical tire and vehicle models improve preventive adhesion estimation at low load, enabling earlier anti-skid action.

Dual extended Kalman filters estimate tire stiffness and vehicle speed to improve traction torque accuracy without added sensors.

Driver alerts and bypassable holdbacks help manage EHC warmup during cold starts, reducing emissions when catalyst heating is inefficient.

Air spring pressure and load-change detection keep vehicle mass estimates accurate as loads or trailer counts change, even without sensors on every axle.

Pitch angle derived from speed, acceleration, and driving force estimates front-rear axle load distribution without dedicated sensors.

Distance-based mode switching in a bidirectional vehicle balances component wear across both ends, extending service life and reducing maintenance.

Active yaw maneuvers use yaw rate data and damping ratios to estimate trailer sway critical speed without costly hitch angle sensors.

Dynamic thresholds for steering angle and target acceleration help autonomous vehicles prevent unsafe turns and acceleration across varying speeds.

Switching rear-side warning zones when a trailer is mounted helps prevent false collision alerts while preserving obstacle detection accuracy.

Off-lattice planning uses pseudo-trailer-configurations and ML cost-to-go estimates to improve real-time trailer path feasibility and quality.

Combining vehicle dynamics force sensing with a tire model improves tire-road friction estimation for low-grip detection and vehicle control.

Real-time supervisory control adjusts axle and wheel torque constraints to manage tire slip, improve stability, and reduce control complexity.

Overlapping vehicle areas and drag-and-drop controls let one screen manage platoon joining, departure, line adjustment, and state display.

A towing sensor hides incompatible sport or towing modes so drivers can only select travel settings that match the current vehicle state.

Stored active-set constraint states cut repeated tire-force optimization steps in vehicle control when center-of-gravity inputs change.

A vehicle condition estimating device calculates longitudinal centroid position using stability factors and wheel cornering powers.

A vehicle control system calculates trailer center of mass and moment of inertia using radar time-of-flight and Doppler shift data.