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The JWT signature verification failed. Check the signing key and the algorithm.
Error code: JWTInvalidSignature
Exception: InvalidSignatureError
Message: Signature verification failed
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
decoded = jwt.decode(
jwt=token,
...<2 lines>...
options=options,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
decoded = self.decode_complete(
jwt,
...<8 lines>...
leeway=leeway,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
decoded = self._jws.decode_complete(
jwt,
...<3 lines>...
detached_payload=detached_payload,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
self._verify_signature(
~~~~~~~~~~~~~~~~~~~~~~^
signing_input,
^^^^^^^^^^^^^^
...<4 lines>...
options=merged_options,
^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
raise InvalidSignatureError("Signature verification failed")
jwt.exceptions.InvalidSignatureError: Signature verification failedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
SimLingo CARLA Dataset (Raw, 4Hz)
Raw driving data from CARLA simulator. No transformations or derived fields - all original measurements preserved as-is.
Dataset Summary
- Source: SimLingo (CVPR 2025)
- Scale: 228,757 frames (23 shards)
- Frame Rate: 4 FPS
- Resolution: 1024x512 RGB
- Routes: Complete driving episodes (routes never split across shards)
Column Schema
Core Fields
| Column | Type | Description |
|---|---|---|
route_id |
string | Route identifier |
frame_idx |
int32 | Frame index within route |
image |
bytes | Original JPEG image bytes |
Control Signals (Raw)
| Column | Type | Description |
|---|---|---|
steer |
float32 | Steering [-1, 1] |
throttle |
float32 | Throttle [0, 1] |
brake |
bool | Brake applied |
Vehicle State
| Column | Type | Description |
|---|---|---|
speed |
float32 | Current speed (m/s) |
target_speed |
float32 | Target speed |
speed_limit |
float32 | Speed limit |
theta |
float32 | Heading angle |
angle |
float32 | Angle to target |
Navigation
| Column | Type | Description |
|---|---|---|
command |
int32 | Navigation command |
next_command |
int32 | Next navigation command |
pos_global |
string (JSON) | Global position [x, y] |
target_point |
string (JSON) | Target point |
target_point_next |
string (JSON) | Next target point |
aim_wp |
string (JSON) | Aim waypoint |
route |
string (JSON) | Planned route waypoints |
route_original |
string (JSON) | Original route waypoints |
changed_route |
bool | Route was changed |
Hazards & Environment
| Column | Type | Description |
|---|---|---|
junction |
bool | In junction |
vehicle_hazard |
bool | Vehicle hazard detected |
vehicle_affecting_id |
int32 | ID of affecting vehicle |
walker_hazard |
bool | Pedestrian hazard |
walker_affecting_id |
int32 | ID of affecting pedestrian |
light_hazard |
bool | Traffic light hazard |
stop_sign_hazard |
bool | Stop sign hazard |
stop_sign_close |
bool | Stop sign nearby |
walker_close |
bool | Pedestrian nearby |
walker_close_id |
int32 | ID of nearby pedestrian |
speed_reduced_by_obj_type |
string | Object type causing speed reduction |
speed_reduced_by_obj_id |
int32 | Object ID causing speed reduction |
speed_reduced_by_obj_distance |
float32 | Distance to speed-reducing object |
control_brake |
bool | Control brake applied |
Augmentation (from SimLingo)
| Column | Type | Description |
|---|---|---|
augmentation_translation |
float32 | Translation augmentation |
augmentation_rotation |
float32 | Rotation augmentation |
Transforms
| Column | Type | Description |
|---|---|---|
ego_matrix |
string (JSON) | 4x4 ego vehicle transform matrix |
boxes |
string (JSON) | 3D bounding boxes for all objects |
Commentary (Optional)
| Column | Type | Description |
|---|---|---|
commentary |
string | Natural language commentary |
commentary_data |
string (JSON) | Full commentary object with metadata |
Usage
from datasets import load_dataset
import json
ds = load_dataset("TESS-Computer/carla-simlingo-raw", split="train")
sample = ds[0]
print(sample['route_id'])
print(sample['steer'], sample['throttle'], sample['brake'])
print(sample['speed'])
# Parse JSON fields
pos = json.loads(sample['pos_global'])
boxes = json.loads(sample['boxes']) if sample['boxes'] else []
Data Collection
- Simulator: CARLA 0.9.15 (Leaderboard 2.0)
- Expert: PDM-Lite (rule-based, 100% route completion)
- Scenarios: Single-scenario routes with random weather
- Towns: Towns 1-13
Citation
@inproceedings{renz2025simlingo,
title={SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment},
author={Renz, Katrin and Chen, Long and Arani, Elahe and Sinavski, Oleg},
booktitle={CVPR},
year={2025},
}
License
MIT (dataset processing code). Original data subject to SimLingo and CARLA licenses.
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