Papers › Learning from All Vehicles
Learning from All Vehicles
Dian Chen, Philipp Krähenbühl
In this paper, we present a system to train driving policies from experiences collected not just from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of other agents to create more diverse driving scenarios without collecting additional data. The main difficulty in learning from other vehicles is that there is no sensor information. We use a set of supervisory tasks to learn an intermediate representation that is invariant to the viewpoint of the controlling vehicle. This not only provides a richer signal at training time but also allows more complex reasoning during inference. Learning how all vehicles drive helps predict their behavior at test time and can avoid collisions. We evaluate this system in closed-loop driving simulations. Our system outperforms all prior methods on the public CARLA Leaderboard by a wide margin, improving driving score by 25 and route completion rate by 24 points. Our method won the 2021 CARLA Autonomous Driving challenge. Code and data are available at https://github.com/dotchen/LAV.
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Code
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Code Syntology ran Syntology
5 samples harvested; 0 ran; 0 honoured the contract we drafted; 5 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Autonomous Driving | CARLA Leaderboard | Learning From All Vehicles (LAV) | Driving Score | 61.846 | #5 of 18 | Archive leaderboard | report |
| Autonomous Driving | CARLA Leaderboard | Learning From All Vehicles (LAV) | Infraction penalty | 0.640 | #5 of 18 | Archive leaderboard | report |
| Autonomous Driving | CARLA Leaderboard | Learning From All Vehicles (LAV) | Route Completion | 94.459 | #5 of 18 | Archive leaderboard | report |
| CARLA longest6 | CARLA | Learning from all Vehicle v2 (LAV v2) | Driving Score | 58 | #10 of 21 | Archive leaderboard | report |
| CARLA longest6 | CARLA | Learning from all Vehicle v2 (LAV v2) | Infraction Score | 0.68 | #10 of 21 | Archive leaderboard | report |
| CARLA longest6 | CARLA | Learning from all Vehicle v2 (LAV v2) | Route Completion | 83 | #10 of 21 | Archive leaderboard | report |
| CARLA longest6 | CARLA | Learning from all Vehicles v1 (LAV v1) | Driving Score | 33 | #17 of 21 | Archive leaderboard | report |
| CARLA longest6 | CARLA | Learning from all Vehicles v1 (LAV v1) | Infraction Score | 0.51 | #17 of 21 | Archive leaderboard | report |
| CARLA longest6 | CARLA | Learning from all Vehicles v1 (LAV v1) | Route Completion | 70 | #17 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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