Timed lane change notifications remind occupants of delayed commands, helping automated driving wait for safe conditions without surprise maneuvers.
A single in-cab display merges dump body, hydraulic, suspension, and camera data so operators can monitor and control loading safely.
Direct feed-forward control uses trajectory states and a vehicle dynamics model to avoid inverse-dynamics filters, cutting delay and complexity.
Competing vehicle kinematic plans are prioritized through centralized arbitration to block interference and keep intended operations stable.
A hybrid vehicle dynamics model combines physical equations with neural networks to improve prediction accuracy and extrapolation for driver assistance.
Direct steering output and shared control data speed vehicle motion control while coordinating braking and driving for smoother travel.
Suppressing noncritical vehicle images on the HUD while toning them down on the meter display reduces clutter and driver distraction.
Kinematic cues in steering, braking, and acceleration make assisted-to-automated mode changes perceptible and reduce driver mode confusion.
Incremental brake correction using limited slip differential transfer torque preserves yaw moment response and continuity during cornering.
Driver-set warning timing lets driving assistance issue alerts only within accepted timing ranges, improving safety support without added annoyance.
Simulated replay of planned trajectories and agent behavior helps judge whether autonomous-driving disengagements were necessary and improves driver feedback.
Torque limiting on selected wheels suppresses slip-driven speed overestimation in 4WD vehicles, improving control accuracy and stability.
When primary driving hardware fails, control shifts to a reduced-resource backup while healthy hardware is reconfigured into new redundancy.
Tiered failure flags and an aggregator node let autonomous vehicles degrade safely instead of forcing immediate shutdowns.
Driver reactions are used to tune abnormal driving alert thresholds, reducing overaction or underreaction and improving confidence.
Simultaneous optimization of nominal and abort trajectories keeps vehicle maneuvers efficient while preserving a safe fallback path.
A vehicle manager maps ADAS application IDs to permitted actuators, preventing wrong actuator activation and unstable vehicle motion.
When a sensor fails, the vehicle keeps safe autonomous functions active by disabling only affected driving conditions or switching sensors.
A 3D execution image separates lane keeping status from lane markings, making control state easier for occupants to recognize.