--- pretty_name: ETS2 Truck Self-Driving Dataset license: other task_categories: - robotics - reinforcement-learning tags: - autonomous-driving - self-driving - imitation-learning - behavioral-cloning - euro-truck-simulator-2 - ets2 - driving - telemetry - webdataset configs: - config_name: default data_files: - split: train path: ets2-train-*.tar - split: val path: ets2-val-*.tar --- # ETS2 Truck Self-Driving Dataset Synchronized screen-capture video and vehicle telemetry recorded from human driving in **Euro Truck Simulator 2 (ETS2)**, packaged for end-to-end driving (imitation learning / behavioral cloning). Each frame pairs a 1080p game screenshot with the full SCS telemetry snapshot taken at the same instant — the control signals (steering, throttle, brake) and vehicle state needed to learn or evaluate a driving policy. If you want to load a single recording locally, jump to [Usage](#usage). If you want to understand what each field means, see [Telemetry fields](#telemetry-fields). ## At a glance - **Modalities:** RGB video + per-frame structured telemetry - **Frame rate:** 10 FPS, video and telemetry aligned 1:1 - **Frame size:** 1920×1080 (H.264, `yuv420p`) - **Format:** [WebDataset](https://github.com/webdataset/webdataset) — `.tar` shards of ~1 GB each - **Splits:** `train`, `val` ## Dataset structure The dataset is a set of WebDataset shards named `ets2-{split}-{index}.tar` (e.g. `ets2-train-000000.tar`). Each shard is a plain tar of complete recording **sessions**. A session is one continuous driving clip (15–90 s) stored as three members that share a basename (the WebDataset *key*); the extension names the field: | Member | Contents | | --------------- | --------------------------------------------------------------------------------------------------------------- | | `{key}.mp4` | H.264 video, 1920×1080, 10 FPS | | `{key}.bin.zst` | zstd-compressed raw telemetry: a fixed **32 KiB** SCS shared-memory block per frame, concatenated in frame order | | `{key}.json` | session meta needed to interpret the telemetry (see [Session meta](#session-meta)) | The telemetry bin is the only compressed member. After zstd decompression it is `32768 × N` bytes for an `N`-frame video; frame `i` corresponds to bytes `[i*32768 : (i+1)*32768]`, the verbatim SCS plugin shared memory at that frame. ### Telemetry fields Fields are decoded from the 32 KiB block by byte offset. The block is stored verbatim, so it carries the entire SCS shared-memory layout; the fields below are the ones already wired up for driving. Angles follow the SCS convention of **turns** (1 turn = 360°). | Field | Type | Meaning | | ---------------------------------------------------------------------------------- | ----- | -------------------------------------------------------------- | | `game_steer` | float | Effective steering, `[-1, 1]` | | `game_throttle` | float | Effective throttle, `[0, 1]` | | `game_brake` | float | Effective brake, `[0, 1]` | | `speed` | float | Truck speed in m/s (negative when reversing) | | `cruise_control_speed` | float | Cruise-control set speed in m/s (0 when off) | | `angular_velocity_y` | float | Yaw rate around truck-Y in turns/s | | `cabin_aa_z` | float | Cabin angular acceleration around truck-Z in turns/s² | | `coordinate_x` / `coordinate_y` / `coordinate_z` | float | World position (ETS2 left-handed frame) | | `rotation_x` / `rotation_y` / `rotation_z` | float | Heading / pitch / roll in turns (heading 0 = north) | | `time_ms` | int | Simulation time in ms; **stops** while the game is paused | | `simulated_time_ms` | int | Simulation time in ms; **keeps advancing** while paused | | `sdk_active` / `paused` / `on_job` / `attached` | bool | SDK active, game paused, delivery job active, trailer attached | | `wear_engine` / `wear_transmission` / `wear_cabin` / `wear_chassis` / `wear_wheels` | float | Component wear, `[0, 1]` | | `cargo_damage` | float | Current cargo damage, `[0, 1]` | | `route_distance` | float | Remaining planned route distance in m | | `planned_distance_km` | int | Initial route length in km (constant for the trip) | | `job_income` | int | Job payment in EUR (constant for the trip) | | `plugin_revid` / `sdk_version_major` / `sdk_version_minor` | int | Telemetry plugin / SDK versions | For driving you typically use the screenshot plus `speed` and pose as inputs and `game_steer` / `game_throttle` / `game_brake` as labels. ### Session meta `{key}.json` is a small object describing how to read the telemetry bin: | Key | Meaning | | ----------------------------------------- | ------------------------------------------------------- | | `plugin_revid` | Telemetry plugin revision (struct layout version) | | `sdk_version_major` / `sdk_version_minor` | SCS SDK version | | `hfov_deg` | Camera horizontal field of view in degrees (default 71) | ## Data collection Sessions are captured from a live ETS2 window, not reconstructed offline: - **Sampling:** the game window is screen-captured at 10 Hz. Accepted window sizes are 1920×1080, 2560×1440, and 3840×2160; every frame is normalized to 1920×1080. Each screenshot is paired with the SCS telemetry snapshot read at the same tick. - **Event-driven recording.** Frames are buffered with a few seconds of look-ahead, and a clip is only kept around interesting moments rather than recording idle time. Two triggers are active: - *moving* — fires while the truck moves faster than 0.3 m/s, keeping a `[-3 s, +1 s]` window around each moving frame; - *collision* — fires when total component wear jumps between frames (a likely impact), keeping a wider `[-3 s, +12 s]` window. - **Clip length.** Kept sessions are 15–90 s; shorter candidate clips are discarded and longer runs are split. ## Usage ### Stream shards directly WebDataset shards stream without downloading the whole dataset. The telemetry member needs zstd decompression, then slicing into 32 KiB per-frame blocks: ```python import webdataset as wds import zstandard as zstd REPO = "/" # replace with the dataset repo id BASE = f"https://huggingface.co/datasets/{REPO}/resolve/main" url = f"{BASE}/ets2-train-{{000000..000006}}.tar" # adjust to the shard range FRAME_BYTES = 32 * 1024 for sample in wds.WebDataset(url): meta = sample["json"] # bytes — session meta video = sample["mp4"] # bytes — H.264 clip raw = zstd.ZstdDecompressor().decompress(sample["bin.zst"]) n_frames = len(raw) // FRAME_BYTES # frame i telemetry: raw[i*FRAME_BYTES : (i+1)*FRAME_BYTES] ``` ### Decode with the recorder toolkit This dataset is produced by the `ets2-dataset` tooling in this repository, which can also read sessions back. Unpack a shard (plain `tar -xf`) and open any session by pointing at one of its members: ```python from ets2_dataset.data.session import Session with Session("session_20260101_120000_123456.mp4") as session: print(len(session), "frames", session.fps, "fps", session.duration, "s") for frame in session: image = frame.image # [H, W, 3] BGR uint8 t = frame.telemetry steer, throttle, brake = t.game_steer, t.game_throttle, t.game_brake ``` `Session` decodes the video and telemetry together and yields frames in recording order, with `telemetry` exposing the fields above as attributes. ## License and attribution The video frames are screenshots of **Euro Truck Simulator 2**, whose game content is © SCS Software. This dataset is intended for non-commercial research and is distributed under that constraint; using it does not grant any rights to the underlying game assets. Set the `license` field in the metadata above to the terms you intend to release the recorded telemetry and annotations under before publishing.