Eve Quest Book - Summer 2026 (S26)
The Great Objective: Level 5 Robo-taxi Around Campus
WATonomous aims to achieve a fully autonomous Level 5 robo-taxi capable of navigating the University of Waterloo campus by the end of Summer 2026. This involves integrating hardware, software, and cognition systems to enable decision-making directly within the car. Achieving this milestone establishes the groundwork for advanced autonomous vehicle research and real-world applications.
Term Objectives Summary
The objectives for Summer 2026 focus on developing BEVFusion for perception, developing MPC for action, lots of testing around ring road, and preparing the platform for autonomous operation.
-
Hardware Integration
- Hardware Quality of Life
-
Software Modules
- BEVFusion
- Prediction
- HD Map
- Localization
- Local Planner (builds a collision-aware spline)
- Controller (MPPI, MPC)
Term Objectives and Scoring
Hardware Integration
- Hardware Quality of Life
| Score | Criteria |
|---|---|
| 10/10 | No more hardware issues with clean cable management, a tidy car, and documentation. |
| 4/10 | Hide all OSCC boards with proper cable management. |
| 3/10 | E-stop no longer engages parking brake when in normal drive mode. |
| 0/10 | No e-stop improvement. |
Minimum Requirements: E-stop no longer engages parking brake when in normal drive mode 10/10.
Software Modules (All must be on the main branch)
- BEVFusion
| Score | Criteria |
|---|---|
| 10/10 | Fine-tuning BEVFusion with our own dataset from Wato-World annotations. |
| 5/10 | Basic BEVFusion setup with initial integration with sensors producing object tracks. |
| 0/10 | No BEVFusion implementation. |
Minimum Requirements: Basic BEVFusion setup with initial integration with sensors producing object tracks.
- Prediction
| Score | Criteria |
|---|---|
| 10/10 | Fully tested prediction on the ring road. |
| 7/10 | Fix up and integrate prediction with BEVFusion. |
| 0/10 | No prediction implemented. |
Minimum Requirements: Fix up and integrate prediction with BEVFusion 7/10.
- HD Map
| Score | Criteria |
|---|---|
| 10/10 | Ring road HD map fully tested around the entire ring road. |
| 7/10 | Ring road HD map tested partially around ring road. |
| 4/10 | Ring road HD map functional with perception integration. |
| 1/10 | Basic ring road HD map loading and visualization. |
Minimum Requirements: Basic ring road HD map loading and visualization. 0/10.
- Localization
| Score | Criteria |
|---|---|
| 10/10 | Reliable Multi-sensor localization on ring road. |
| 4/10 | Multi-sensor localization functional on ring road. |
| 0/10 | Basic localization using GPS on the ring road. |
Minimum Requirements: Multi-sensor localization functional on ring road for a score of 4/10.
- Local Planner
| Score | Criteria |
|---|---|
| 10/10 | Local planner in monorepo handling dynamic obstacles smoothly. |
| 4/10 | Basic local planning implementation. |
| 0/10 | No local planner. |
Minimum Requirements: Basic local planning for a score of 4/10.
- Controller
| Score | Criteria |
|---|---|
| 10/10 | Advanced controller in monorepo fully tuned and tested on vehicle. |
| 4/10 | Basic controller implementation. |
| 0/10 | No controller implementation. |
Minimum Requirements: Basic controller for a score of 4/10.
Scoring Template
Hardware Integration
| Quest Name | Description | Score |
|---|---|---|
| Hardware Quality of Life | No more hardware issues with clean cable management, a tidy car, and documentation |
Software Modules
| Quest Name | Description | Score |
|---|---|---|
| BEVFusion | Fine-tuning BEVFusion with our own dataset from Wato-World annotations. | |
| Prediction | Fully tested prediction on the ring road | |
| HD Map | Load custom HD maps with regulatory elements | |
| Localization | Multi-sensor localization with cm accuracy | |
| Local Planner | Dynamic obstacle avoidance and smooth navigation | |
| Controller | MPC/MPPI controller for precise vehicle control |