Robovans are autonomous vans designed for both cargo delivery and passenger transport. They sit between robotaxis and robotrucks, offering flexible platforms for last-mile logistics, shared mobility, and campus/airport shuttle services. Major OEMs and startups (Amazon Zoox, Rivian EDV pilots, Ford E-Transit AV trials, Tesla’s 20-seat people mover prototype) are advancing both cargo and passenger configurations.
Segment Taxonomy
| Subtype | Passenger Capacity | Primary Use | Notes |
| Cargo Robovans |
2–3 m³ payload, 1–2 ton GVW |
Parcel, grocery, e-commerce delivery |
Amazon Rivian EDV with autonomy pilots, BrightDrop Zevo, Ford E-Transit AV trials |
| Passenger Robovans (People Movers) |
8–20 passengers |
Campus, airport, event, city loop shuttles |
Tesla 20-seat concept, Navya, EasyMile, May Mobility |
| Dual-Use Platforms |
Configurable interior (cargo or passenger) |
Flexible fleet utilization, MaaS or DaaS |
Emerging designs with modular interiors, useful for mixed operations |
Robovan Hardware & AI Stack
| Layer | Examples | Primary Role |
| Powertrain |
EV van platforms (60–120 kWh), urban range 150–250 miles |
Optimized for city delivery loops and shuttle duty cycles |
| Sensors |
Camera + radar (delivery), camera + LiDAR + radar (passenger safety) |
Provide perception redundancy depending on risk profile |
| Compute Stack |
NVIDIA Drive Orin, Tesla FSD chip, Qualcomm Snapdragon Ride |
Onboard inference, sensor fusion, AV decision logic |
| Networking Stack |
Wi-Fi 6/6E, 5G/LTE V2X, CAN/EtherCAT buses |
Fleet connectivity, OTA updates, telematics |
| Memory & Storage |
16–64 GB RAM, SSD 256 GB–1 TB, edge caches for policies |
Buffer sensor data, support offline autonomy modules |
| LLMs & Agents |
Conversational copilots, natural voice commands, multi-modal reasoning |
Passenger experience (“pick me up at Gate A”), driver-like cargo assist, task planning |
| Fleet AI & Management |
Dispatch optimization, energy/charging coordination, OTA fleet updates |
Enable cost-efficient routing, maximize utilization, keep fleet software current |
| Simulation & Digital Twin |
Urban twins for delivery routes and shuttle loops |
Validate safety, train edge cases, optimize routes |
Market Outlook & Adoption
Robovans are expected to see earlier adoption than robotaxis due to their fleet orientation, predictable routes, and clear ROI in delivery and shuttle contexts. Passenger robovans face the same regulatory hurdles as robotaxis, but controlled environments (airports, campuses) accelerate deployment.
| Rank | Adoption Factor | Drivers | Constraints |
| 1 |
Cargo Robovans (Delivery) |
E-commerce growth, route density, labor cost pressure |
Urban traffic, city regulations, curb access |
| 2 |
Passenger Robovans (People Movers) |
Airports, campuses, event venues, MaaS integration |
Regulatory approvals, safety validation, low ODD complexity required |
| 3 |
Dual-Use Modular Platforms |
Fleet utilization flexibility, quick reconfiguration |
Still conceptual; needs robust modularity standards |