# Design choices by example

This page provides detailed component specifications for each reference configuration of the [Low Speed Autonomy (LSA) guideline](<https://autowarefoundation.github.io/LSA-reference-design-docs/main/>). Use these as starting points for your own build. The ODD scenario IDs, for example `LSA-CAM-0001`, come from the [outdoor ODD definition](<https://autowarefoundation.github.io/LSA-reference-design-docs/main/odd-definition/outdoor/>) of the LSA guideline.

## Configuration overview

| Configuration | ECU | Primary LiDAR | Localization |
| --- | --- | --- | --- |
| [Campus](<#campus-configuration>) | AGX Orin 32/64GB | Ouster OS1-64 | RTK GNSS |
| [Indoor](<#indoor-configuration>) | AGX Orin | Ouster OSDome | LiDAR SLAM + markers |
| [High-Performance](<#high-performance-configuration>) | x86 + RTX 4090 | OS2 + OSDome | GNSS + SLAM fusion |
| [Budget](<#budget-configuration>) | Orin NX 16GB | Ouster OS0-32 | Single GNSS |
| [Compact](<#compact-configuration>) | Orin Nano/NX | Ouster OSDome | Camera SLAM |

---

## Campus configuration

Balanced configuration for outdoor paved environments with pedestrian interaction.

| Component | Choice | Rationale |
| --- | --- | --- |
| **ECU** | NVIDIA AGX Orin 32/64GB | Up to 275 TOPS, 15-60W, proven Autoware support |
| **LiDAR** | Ouster OS1-64 | 120m range, 64 channels, reliable ROS 2 driver |
| **Cameras** | 2-4 wide-angle | Object classification, sign detection |
| **GNSS** | Dual-antenna RTK | Cm-level accuracy, heading estimation |
| **IMU** | Integrated or standalone | Motion estimation, sensor fusion |

**Performance Targets:**

| Metric | Value |
| --- | --- |
| LiDAR detection | 10 Hz |
| Camera detection | 15 Hz |
| Planning | 10 Hz |
| Control | 50 Hz |
| End-to-end latency | \<200 ms |
| Power (ECU) | \<50W |

**ODD Coverage:** LSA-CAM-0001 to 0040 (normal driving through intersections)

**Real World Examples:** [TalTech iseAuto](<../find-your-reference-design/#taltech-iseauto>), [NC A&amp;T Aggie Auto](<../find-your-reference-design/#nc-at-aggie-auto>)

For detailed ECU specifications, see [ARM-based ECUs](<https://autowarefoundation.github.io/LSA-reference-design-docs/main/hardware-configuration/ECUs/ARM/>) in the LSA guideline.

---

## Indoor configuration

Optimized for GPS-denied warehouse and indoor structured environments.

| Component | Choice | Rationale |
| --- | --- | --- |
| **ECU** | AGX Orin | Same compute as Campus |
| **LiDAR** | Ouster OSDome | 180 deg FOV for narrow aisles, 45m range |
| **Cameras** | Wide-angle + depth | SLAM features, close-range detection |
| **GNSS** | None | GPS-denied environment |
| **Localization** | LiDAR SLAM + markers | AprilTags or reflectors for accuracy |

**Key Differences from Campus:**

- No GNSS dependency
- Wide-FOV sensors for tight maneuvering
- Marker infrastructure for localization accuracy
- Lower speed limits (5-10 kph typical)

For indoor scenarios, see the [indoor ODD](<https://autowarefoundation.github.io/LSA-reference-design-docs/main/odd-definition/indoor/>) of the LSA guideline.

---

## High-performance configuration

Maximum capability for research and complex scenarios.

| Component | Choice | Benefit |
| --- | --- | --- |
| **ECU** | x86 Xeon/Core + RTX 4090 | Maximum compute, 24GB VRAM |
| **LiDAR** | Ouster OS2 (200m) + OSDome | Long range + wide FOV coverage |
| **Cameras** | 6-8 surround cameras | 360 deg visual coverage |
| **GNSS** | Multi-constellation RTK | Maximum accuracy |
| **Localization** | GNSS + LiDAR SLAM fusion | Maximum robustness |

**ODD Coverage:** Full LSA-CAM-0001 to 0082

**Trade-offs:** Higher cost, power consumption (150-450W), complexity

---

## Budget configuration

Cost-optimized with reduced capabilities.

| Component | Choice | Trade-off vs Campus |
| --- | --- | --- |
| **ECU** | Jetson Orin NX 16GB | Lower compute headroom |
| **LiDAR** | Ouster OS0-32 or OS1-32 | Shorter range (35-90m), fewer channels |
| **Cameras** | 1-2 cameras | Reduced coverage |
| **GNSS** | Single-antenna | Lower heading accuracy |

**ODD Limitations:** LSA-CAM-0001 to 0020 (basic scenarios only)

**Best For:** Simple fixed routes, low-traffic environments

---

## Compact configuration

For small vehicles with space and weight constraints.

| Component | Choice | Rationale |
| --- | --- | --- |
| **ECU** | Orin Nano or NX | Small form factor, 7-25W |
| **LiDAR** | Ouster OSDome | Compact, wide FOV |
| **Carrier** | Integrated board | Minimizes size and cabling |
| **Cameras** | 1-2 compact modules | Essential coverage |
| **Localization** | Camera SLAM | No external infrastructure |

**Best For:** Sidewalk delivery robots, small indoor AGVs

---

## Component selection reference

### ECU selection by criteria

| Criteria | Recommendation |
| --- | --- |
| Balanced cost/performance | AGX Orin 32GB |
| Minimum cost | Orin NX 16GB |
| Maximum compute | x86 + RTX 4090 |
| Minimum size/power | Orin Nano |
| Industrial environment | Neousys 9160-GC |
| Wide temperature range | Crystal Rugged AVC series |

For full ECU specifications:

- [ARM-based ECUs](<https://autowarefoundation.github.io/LSA-reference-design-docs/main/hardware-configuration/ECUs/ARM/>)
- [x86-based ECUs](<https://autowarefoundation.github.io/LSA-reference-design-docs/main/hardware-configuration/ECUs/x86_64/>)

### LiDAR selection by application

| Application | Recommendation | Range | Channels |
| --- | --- | --- | --- |
| Outdoor campus | Ouster OS1-64 | 90m | 64 |
| Long-range outdoor | Ouster OS2-128 | 200m | 128 |
| Indoor/warehouse | Ouster OSDome | 20m | 128 (180 deg FOV) |
| Budget outdoor | Ouster OS0-32 | 35m | 32 |
| Compact vehicles | Ouster OSDome | 20m | 128 |

For sensor options, see [Sensors and actuators](<https://autowarefoundation.github.io/LSA-reference-design-docs/main/hardware-configuration/Sensors-and-Actuators/>) in the LSA guideline and the [reference hardware](<../../reference-hw/index.md>) list.

### Localization method by environment

| Environment | Primary Method | Backup/Enhancement |
| --- | --- | --- |
| Outdoor, open sky | RTK GNSS | NDT matching |
| Outdoor, urban canyon | GNSS + LiDAR SLAM | Visual odometry |
| Indoor, structured | LiDAR SLAM | Marker-based (AprilTags) |
| Indoor, featureless | Marker-based | Wheel odometry |

### Middleware selection

| Scenario | Recommendation | Rationale |
| --- | --- | --- |
| Wireless operation | Zenoh | Better packet handling |
| Wired, lowest latency | Cyclone DDS | Minimal overhead |
| Enterprise integration | RTI Connext | Commercial support |

For middleware setup, see [Middleware configuration](<https://autowarefoundation.github.io/LSA-reference-design-docs/main/software-configuration/middleware-configuration/>) in the LSA guideline.

---

## Next steps

- Use [Find your reference design](<../find-your-reference-design/index.md>) to see real-world examples for each configuration
- Review the [hardware configuration](<https://autowarefoundation.github.io/LSA-reference-design-docs/main/hardware-configuration/>) of the LSA guideline for component details
- Follow the [software configuration](<https://autowarefoundation.github.io/LSA-reference-design-docs/main/software-configuration/>) of the LSA guideline for setup guides
