---
title: 'DTPQA: Distance-Annotated Traffic Perception QA'
url: https://www.emergentmind.com/topics/distance-annotated-traffic-perception-question-answering-dtpqa
type: topic
---

# DTPQA: Distance-Annotated Traffic Perception QA

Distance-Annotated Traffic Perception Question Answering (DTPQA) is a visual question answering (VQA) benchmark specifically constructed to quantitatively evaluate the perception capabilities of Vision-Language Models (VLMs) within traffic scenarios, with a primary emphasis on the effects of object distance on perception accuracy. Designed to exclude high-level reasoning and instead isolate fundamental perception, DTPQA addresses critical needs in automated driving, where reliable detection and identification of objects are indispensable both at close (≤ 20 m) and far (> 30 m) ranges. The benchmark provides a structured, distance-stratified approach to probing perception-only skills using trivial yet traffic-relevant questions, with rigorous annotation and data generation protocols [2511.13397].

## 1. Motivation and Scope

Automated driving systems require precise, distance-aware perception to guide safety-critical behaviors such as braking and lane changes. Preexisting VQA datasets for traffic scenes typically entangle perception with commonsense and world-knowledge reasoning, obscuring the fine-grained measurement of fundamental visual understanding. Moreover, almost no VQA benchmarks systematically assess perception degradation with increasing object distance. DTPQA explicitly addresses these gaps by:

- Providing exclusively perception-driven, multiple-choice VQA tasks framed around basic but crucial driving questions (e.g., pedestrian presence, traffic light color).
- Annotating every queried object’s precise distance from the camera, allowing for direct stratified evaluation across binned intervals (5, 10, 20, 30, 40, 50 m and "Negative"/absent).
- Offering a dual-modal composition: a synthetic CARLA-based dataset (DTP-Synthetic) with consistent ground-truth control, and a real-image set (DTP-Real) curated from the nuScenes dataset.

## 2. Dataset Design and Construction

DTPQA consists of 19,149 total samples, split between DTP-Synthetic (9,368 samples) and DTP-Real (9,781 samples). Each sample comprises an RGB image, a posed question, a ground-truth answer, discrete and precise distance annotations, and category-specific metadata.

**DTP-Synthetic:** Generated in CARLA v0.9.15 (maps Town01–07, Town10HD, Town12, Town15), scenarios are randomized in terms of time (hours 6:00–18:00) and weather (Clear, Rain, Wet, Clouds, Fog). A single 90° FOV camera mounted on a standard “ego-vehicle” at 1.4 m height captures scenes after objects are spawned at exact distances. Each object instance is placed at one of the six target distances ($d \in \{5, 10, 20, 30, 40, 50\}$ m), with minor Gaussian noise (σ = 1.2 m where relevant). Images and associated metadata are subject to manual validation, ensuring high-quality, artifact-free samples.

**DTP-Real:** Frames are sourced from nuScenes (Motional, 2019), using front and surround RGB cameras. Object distances ($d_{exact}$) are computed from nuScenes’ 3D bounding box annotations and binned to the nearest of $\{5, 10, 20, 30, 40, 50\}$ m for inclusion (excluding samples where $d_{exact} > 55$ m). Sample selection ensures balanced per-category and per-distance answer distributions, exploiting nuScenes metadata such as visibility and object class.

**Annotation Schema:** Each dataset sample is stored as a JSON entry containing: image path, question, answer, discrete ("distance") and, if applicable, floating-point ("precise_distance") metrics, with fields for town, weather, and other category-specific details.

**Distance Binning:** The binning strategy minimizes $|d_{exact} - b|$ over $b \in \{5,10,20,30,40,50\}$ m. Samples exceeding 55 m are omitted.

## 3. Category Structure and Question Taxonomy

DTPQA questions are chosen to be trivial in semantic content yet crucial for safety and decision-making:

| Category                        | Question Template                                  | Answer Set                     |
|----------------------------------|----------------------------------------------------|-------------------------------|
| Single-pedestrian presence       | Are there any pedestrians crossing the road?       | {Yes, No}                     |
| Pedestrian direction             | Is the pedestrian walking to the left or right?    | {Left, Right}                  |
| Multiple-pedestrian count        | How many people are ahead?                         | {0,1,2,3}                      |
| Indicator (blinker) side         | Which turn signal is active on the truck ahead?    | {Left, Right, None}            |
| Traffic-light colour (synthetic) | What colour is the traffic light?                  | {Red, Yellow, Green}           |
| Traffic-sign type (synthetic)    | What is the shown road sign?                       | {SpeedLimit, Stop, …}          |

DTP-Synthetic includes all six categories, while DTP-Real comprises the four categories possible given nuScenes annotation coverage. Each distance bin and category combination is balanced across all answer options, except for a minor, statistically insignificant 12/11/11 split in Cat.5-Synth at 10 m.

## 4. Dataset Statistics

Distribution per distance bin and modality is as follows:

| Distance (m) | DTP-Synth | DTP-Real | Combined |
|--------------|-----------|----------|----------|
| 5            | 1,350     | 379      | 1,729    |
| 10           | 1,434     | 1,280    | 2,714    |
| 20           | 1,556     | 865      | 2,421    |
| 30           | 1,556     | 2,329    | 3,885    |
| 40           | 1,556     | 2,310    | 3,866    |
| 50           | 1,556     | 2,218    | 3,774    |
| Negative     | 360       | 400      | 760      |
| **Total**    | 9,368     | 9,781    | 19,149   |

Each sample’s representation allows for precise calibration of VLM performance as a function of distance. Negative cases (object absent) further ensure robustness to object misdetection.

## 5. Evaluation Protocol and Baselines

Benchmark evaluation proceeds via standard accuracy (ACC) and chance-corrected accuracy (CCA):

\[
\mathrm{CCA} = \frac{\mathrm{ACC} - p_{\mathrm{chance}}}{1 - p_{\mathrm{chance}}} \times 100\%
\]

where $p_{\mathrm{chance}} = 1 / |\text{AnswerSpace}|$. Analyses stratify ACC and CCA by distance bin.

Ten baseline VLMs were assessed: nine state-of-the-art “small” models (e.g., BLIP-2, InstructBLIP, MiniGPT-4, several LLaVA variants) and one large model. All were evaluated zero-shot, with no task-specific tuning. Prompts followed the canonical:  
“Q: <question>\nA: (a) <opt1> (b) <opt2> … Which is correct?”

Results reveal that humans reach ≥ 95% CCA across all categories. VLMs show CCA values between 20% and 80% (task-dependent), with spatial categories (e.g., pedestrian direction, blinker side) demonstrating particular degradation at ≥ 30 m. Performance generally decreases linearly with log-distance: for example, ACC at 5 m is approximately 80–90%, falling to 30–40% at 50 m. Performance is modestly higher on synthetic data, with real images being more challenging due to greater scene complexity and variable lighting.

## 6. Data Generation Pipeline and Extension

All dataset generation scripts are released. The repository comprises:

- `dtp_synth/`: CARLA-based synthetic generator; includes configuration (map list, weather presets, distance bins), main pipeline (`generate_synth.py`), and utility functions for precise object placement.
- `dtp_real/`: nuScenes annotation and filtering; scripts for keyframe selection, distance binning, and sample export (`extract_real.py`, `bin_distances.py`).
- Output structure stores JPEGs and a master annotation JSON.

Extending the benchmark is facilitated via:
- Addition of new question categories or object classes with custom routines in the respective pipelines.
- Modification of distance bins via configuration changes.
- Adjustment of weather or time-of-day conditions through configuration files, with all such parameters recorded at the sample level.

## 7. Insights, Limitations, and Future Directions

Current DTPQA tasks emphasize basic perception cues; extension to more complex scenarios—such as occlusion, adverse weather, and multimodal (e.g., RGB + LiDAR) fusion—remains open. DTP-Real’s range is inherently limited by nuScenes (≤ 55 m, 6 cameras), suggesting that inclusion of datasets like Waymo or Argoverse could diversify perspective and range. All published VLM results to date are strictly zero-shot; while fine-tuning on DTPQA could significantly improve results, it may introduce overfitting to dataset-specific biases.

Additional annotations (e.g., weather, town for synthetic; variance in precise distance for real) enable secondary analyses on weather robustness and sensitivity to object position. Anticipated extensions include evaluations with dynamic video-VQA, robust prompt-engineering protocols, and integration of other sensor modalities [2511.13397].

Source: https://www.emergentmind.com/topics/distance-annotated-traffic-perception-question-answering-dtpqa