Papers › Exploring the Limitations of Behavior Cloning for Autonomous Driving

Exploring the Limitations of Behavior Cloning for Autonomous Driving

18 Apr 2019ICCV 2019 10arXiv:1904.08980archive 2025-07-28

Felipe Codevilla, Eder Santana, Antonio M. López, Adrien Gaidon

Driving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation learning can, in theory, leverage data from large fleets of human-driven cars. Behavior cloning in particular has been successfully used to learn simple visuomotor policies end-to-end, but scaling to the full spectrum of driving behaviors remains an unsolved problem. In this paper, we propose a new benchmark to experimentally investigate the scalability and limitations of behavior cloning. We show that behavior cloning leads to state-of-the-art results, including in unseen environments, executing complex lateral and longitudinal maneuvers without these reactions being explicitly programmed. However, we confirm well-known limitations (due to dataset bias and overfitting), new generalization issues (due to dynamic objects and the lack of a causal model), and training instability requiring further research before behavior cloning can graduate to real-world driving. The code of the studied behavior cloning approaches can be found at https://github.com/felipecode/coiltraine .

PaperPDFConference PDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="1904.08980")

Code

Syntology Ran 1 of 8 code samples harvested from 1 repository linked to this paper; 7 have no recorded run. Of those that ran: 1 ran · our draft was wrong.

By repository: official repository: 8 samples from 1 repository, 1 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

felipecode/coiltraine officialmentioned in paperMIT report
Suryavf/SelfDrivingCar mentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

8 samples harvested; 1 ran; 0 honoured the contract we drafted; 7 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.

1ran · our draft was wrong
7unverified

Licence: 0 of the 8 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from felipecode/coiltraine. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

get_actor_display_name felipecode/coiltraine/model_view/carla09interface.py official repository ran · our draft was wrong MIT (permissive) · c882c504d11f2b36 · report
CoILModel felipecode/coiltraine/network/coil_model.py official repository unverified MIT (permissive) · c04d66ffd473a47d · report
l1 felipecode/coiltraine/network/loss.py official repository unverified MIT (permissive) · 4e12f4e418b881c1 · report
l1_attention felipecode/coiltraine/network/loss.py official repository unverified MIT (permissive) · 482100396fe09069 · report
l2 felipecode/coiltraine/network/loss.py official repository unverified MIT (permissive) · 55241c7aaf9ca5a1 · report
normalize felipecode/coiltraine/network/loss_functional.py official repository unverified MIT (permissive) · c1443550f912c8aa · report
weight_decay_l1 felipecode/coiltraine/network/loss_functional.py official repository unverified MIT (permissive) · c0c42913959fe85f · report
weight_decay_l2 felipecode/coiltraine/network/loss_functional.py official repository unverified MIT (permissive) · 6d499286e3718d0e · report

Tasks

Autonomous DrivingImitation Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Autonomous Driving CARLA Leaderboard CILRS Driving Score 5.37 #18 of 18 Archive leaderboard report
Autonomous Driving CARLA Leaderboard CILRS Infraction penalty 0.55 #18 of 18 Archive leaderboard report
Autonomous Driving CARLA Leaderboard CILRS Route Completion 14.40 #18 of 18 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.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections