⚡ Autonomous Fleets

Robotrucks, Vans & Buses


Energy Needs:
Robotrucks & buses consume 10–20x the energy of a robotaxi per vehicle. Even 10,000 autonomous trucks would demand ~5–10 GWh/day charging capacity.

Robotrucks, autonomous vans, and robotbuses are the heavy-duty cousins of robotaxis, designed for freight, last-mile delivery, and public transit. These vehicles operate in structured fleet environments (depots, logistics hubs, fixed routes) and have larger battery packs, higher energy demands, and different autonomy stacks compared to passenger robotaxis.

Robotrucks, vans, and buses are critical to the future of electrified logistics and public transport. Their adoption is slower than robotaxis due to higher energy demands and infrastructure upgrades, but they may have greater economic impact once scaled. Expect robotrucks to lead on highway corridors (low complexity, high ROI), vans to proliferate in last-mile delivery, and buses to integrate with smart city MaaS platforms.


Why They Matter

  • Freight Efficiency: Long-haul autonomous trucks reduce driver cost and increase utilization.
  • Urban Logistics: Vans enable last-mile e-commerce delivery at scale.
  • Public Transit: Autonomous buses provide cost-efficient, on-demand routes.
  • Electrification Impact: Large batteries + megawatt charging reshape grid and depot infrastructure.

Key Use Cases

  • Robotrucks (Class 8/Long-Haul): Highway freight, platooning, autonomous logistics corridors.
  • Robovans: Last-mile package delivery, grocery, retail logistics.
  • Robobuses: Fixed-route public transit, corporate campuses, airports, universities.

Hardware & Charging Stack

Battery Packs (Typical)

  • Trucks: 500–1000+ kWh (Tesla Semi, Daimler eCascadia, Einride).
  • Buses: 300–600 kWh (Proterra, BYD, NFI, Volvo).
  • Vans: 100–150 kWh (Rivian EDV, BrightDrop, Arrival).

Sensors & Autonomy

  • Robotrucks: Long-range LiDAR, radar, and camera fusion for highway driving.
  • Buses: Pedestrian detection, precise curb docking, safe passenger flow.
  • Vans: Dense urban mapping + obstacle avoidance.

AI & Compute Stack

  • Inference Chips: NVIDIA Drive Orin, Qualcomm Ride, custom AV SoCs.
  • Storage: Onboard SSD for sensor/telemetry buffering, cloud for fleet-level training.
  • Networking: 5G / LTE / V2X for fleet coordination + traffic management.
  • Fleet AI: Depot routing, energy management, maintenance scheduling, integration with logistics platforms.
  • LLM Agents (protoype): Natural language dispatch, passenger interface (bus shuttles).

Storage

  • Local SSD for sensor data buffering.
  • Cloud upload of high-priority telemetry for fleet training.

Fleet AI & Management:

  • Centralized dispatch + routing optimization.
  • Energy management linked to charging depots and grid.
  • OTA updates for FSD/AV software.

Memory & Storage Stack

  • RAM: 16–64 GB, sometimes LPDDR5 for low latency.
  • Local Storage: 256 GB – 2 TB SSD for OS, control software, and local datasets.
  • Edge Caches: Store pre-trained LLMs and reinforcement learning policies for offline operation.

Networking Stack

  • Local: High-speed internal bus (CAN, EtherCAT, or custom real-time links).
  • External: Wi-Fi 6/6E, 5G (private network for industrial deployments), Ethernet docked.
  • Fleet/Cloud Integration: Telemetry, updates, and LLM access streamed via secure links.

LLMs & Agents:

  • Uses cloud-connected large language models (GPT, Gemini, or proprietary).
  • Can speak, listen, and respond in natural conversation.
  • Often paired with text-to-speech (TTS) and speech-to-text (STT) engines for human interaction.
  • Multi-modal perception + reasoning (CV + NLP).
  • Can plan multi-step tasks, ask clarifying questions, and hand off to cloud LLM when needed.
  • In industrial deployments, usually bounded by safety filters and task-specific constraints.




Software Solutions