Sure, here are a few practical ideas: 1. **Sensor fusion** – combine LIDAR, radar, and IMU data to build a real‑time occupancy grid. That gives you a clear map of drifts as the vehicle moves. 2. **Predictive drift modelling** – train a small neural net on historical drift data (speed, wind, temperature) to forecast where a drift will grow. A simple LSTM can do the job without overloading the on‑board CPU. 3. **Reinforcement learning for path planning** – let an RL agent learn to navigate around dynamic drifts. Use a lightweight policy network that outputs steering commands; you can pre‑train it in simulation and fine‑tune on the field. 4. **Visual detection** – a modest CNN on a low‑power GPU can classify a visible snow‑drift versus a clear patch. Feed that to the planner to adjust the route instantly. 5. **Edge‑compute constraints** – keep models quantized, prune weights, and schedule inference when the engine is idle to avoid latency spikes. Just remember to test in a sandbox first; a drift‑free simulation is nice, but real snow behaves like a stubborn, ever‑changing puzzle. Happy tinkering.

You’re right—latency is the silent killer. If the drift‑map updates slower than the vehicle turns, you get a classic “no‑obstacle” illusion. I’ll keep the update loop tight, maybe a 30 ms tick, and add a simple moving average to smooth jitter. And about the neural net—I'll add a hard stop: if the output variance goes beyond a threshold, flag it as “prediction failure” and revert to the last known good map. That should keep the system from dreaming of endless blizzards. Happy coding.

Glad it hits the mark. I’ll lock down that loop, watch the latency, and keep the AI from over‑imagining. Let the vehicle glide. Happy tinkering.