Update README.md

README.md

@@ -15,11 +15,53 @@ tags:
 - human-robot-interaction
 - explanableAI
 - reasoning
-pretty_name: d
-Bias and Representation: Our group of 74 annotators includes 44 males and 30 females, ranging in age from 19 to 77. The age distribution is balanced across three groups — 19 to 30, 31 to 54, and 55 and older. All annotators hold valid U.S. driver’s licenses. Their driving experience varies, with approximately 25 percent driving around 5,000 miles per year, 40 percent driving about 10,000 miles per year, and the remainder exceeding 15,000 miles annually. This diversity provides a broad range of perspectives for pedestrian intent estimation and reasoning. However, we acknowledge that the dataset is U.S.-centric, as the driving scenes were collected from multiple cities across the United States, and may not generalize to global contexts.
-Privacy and Consent: The dataset does not contain any personally identifiable information. All data was collected from public environments and anonymized where necessary. Users are responsible for ensuring compliance with local data protection regulations when using or sharing the dataset. All video and annotation data were reviewed and approved by the Indiana University legal department under an IRB-approved protocol. Users must agree to use the dataset solely for research purposes. While the current release does not include face or license plate blurring, we acknowledge this limitation and plan to implement de-identification and anonymization protocols in future updates.
-Social Impact and Ethical Considerations: The dataset was collected with careful consideration of data ownership and consent. However, users should remain mindful of potential ethical concerns when deploying models trained on this data, particularly in sensitive domains such as surveillance, hiring, or healthcare. We recognize the risk of overfitting models to subjective human reasoning. To help mitigate this, PSI includes annotations from a diverse group of annotators and preserves disagreements across labels. This approach supports the development of models that can reason under uncertainty rather than relying solely on consensus.
-Limitations: This dataset provides valuable insights into driver-pedestrian interactions and reasoning for autonomous driving research. However, users should be aware of several limitations. (1) The dataset is smaller than some large-scale benchmarks, which may affect generalization in data-intensive models. (2) Geographic scope — all videos were collected in U.S. cities, which may limit applicability to regions with different traffic rules, infrastructure, or pedestrian behavior. (3) Demographic representation — the dataset may not fully capture the diversity of pedestrian demographics, such as age, mobility, or clothing styles, which could introduce bias in behavior prediction or reasoning. (4) Annotation subjectivity — reasoning or intention annotations may reflect annotator bias or interpretation variability, especially in ambiguous scenarios. (5) Temporal relevance — the videos may not reflect recent advancements in camera technology, changes in traffic behavior, vehicle systems, or pedestrian norms, which could affect the dataset’s relevance for current autonomous driving systems.
+pretty_name: psi-2.0
+Bias and Representation: >-
+  Our group of 74 annotators includes 44 males and 30 females, ranging in age
+  from 19 to 77. The age distribution is balanced across three groups — 19 to
+  30, 31 to 54, and 55 and older. All annotators hold valid U.S. driver’s
+  licenses. Their driving experience varies, with approximately 25 percent
+  driving around 5,000 miles per year, 40 percent driving about 10,000 miles per
+  year, and the remainder exceeding 15,000 miles annually. This diversity
+  provides a broad range of perspectives for pedestrian intent estimation and
+  reasoning. However, we acknowledge that the dataset is U.S.-centric, as the
+  driving scenes were collected from multiple cities across the United States,
+  and may not generalize to global contexts.
+Privacy and Consent: >-
+  The dataset does not contain any personally identifiable information. All data
+  was collected from public environments and anonymized where necessary. Users
+  are responsible for ensuring compliance with local data protection regulations
+  when using or sharing the dataset. All video and annotation data were reviewed
+  and approved by the Indiana University legal department under an IRB-approved
+  protocol. Users must agree to use the dataset solely for research purposes.
+  While the current release does not include face or license plate blurring, we
+  acknowledge this limitation and plan to implement de-identification and
+  anonymization protocols in future updates.
+Social Impact and Ethical Considerations: >-
+  The dataset was collected with careful consideration of data ownership and
+  consent. However, users should remain mindful of potential ethical concerns
+  when deploying models trained on this data, particularly in sensitive domains
+  such as surveillance, hiring, or healthcare. We recognize the risk of
+  overfitting models to subjective human reasoning. To help mitigate this, PSI
+  includes annotations from a diverse group of annotators and preserves
+  disagreements across labels. This approach supports the development of models
+  that can reason under uncertainty rather than relying solely on consensus.
+Limitations: >-
+  This dataset provides valuable insights into driver-pedestrian interactions
+  and reasoning for autonomous driving research. However, users should be aware
+  of several limitations. (1) The dataset is smaller than some large-scale
+  benchmarks, which may affect generalization in data-intensive models. (2)
+  Geographic scope — all videos were collected in U.S. cities, which may limit
+  applicability to regions with different traffic rules, infrastructure, or
+  pedestrian behavior. (3) Demographic representation — the dataset may not
+  fully capture the diversity of pedestrian demographics, such as age, mobility,
+  or clothing styles, which could introduce bias in behavior prediction or
+  reasoning. (4) Annotation subjectivity — reasoning or intention annotations
+  may reflect annotator bias or interpretation variability, especially in
+  ambiguous scenarios. (5) Temporal relevance — the videos may not reflect
+  recent advancements in camera technology, changes in traffic behavior, vehicle
+  systems, or pedestrian norms, which could affect the dataset’s relevance for
+  current autonomous driving systems.
 ---
 # Pedestrian Situated Intent (PSI) Bencharmark
 This Repository contains the scripts and instructions about preparing the **Pedestrian Situated Intent (PSI) 2.0** dataset. 
@@ -69,11 +111,10 @@ and the train/val/test splits:
 
 
 ## Bias, Privacy, Social Impact, and Limitations Considerations
-- **Bias and Representativeness**: Our 74 annotators (44 males, 30 females) range in age from 19 to 77, with equal distribution across three age groups (19–30, 31–54, >=$55), and all hold valid U.S. driver’s licenses. Driving experience varies: ~25\% drive ~5,000 miles/year, ~40\% drive ~10,000 miles/year, and the remainder >15,000 miles/year. This diversity supports a broad range of perspectives in pedestrian intent estimation and reasoning. However, we acknowledge that the dataset is U.S.-centric, with driving scenes collected from multiple U.S. cities, and may not generalize globally.
-- **Privacy and Consent**: The dataset does not contain personally identifiable information. All data was collected from public environment and anonymized where necessary. Users should still ensure compliance with local data protection regulations in using and sharing the dataset. All video and annotation data were reviewed and approved by the Indiana University legal department under an IRB-approved protocol. Dataset users must agree to use it solely for research purposes. While the current release does not include face or license plate blurring, we acknowledge this limitation and plan to apply de-identification and anonymization protocols in future updates. 
-- **Social Impact and Ethical Considerations**: The dataset was collected with respect to data ownership and consent. However, users should be aware of potential ethical concerns when deploying models trained on this data, especially in [e.g., surveillance, hiring, healthcare]. We recognize the risk of overfitting models to subjective human reasoning. To address this, PSI includes annotations from diverse annotators and preserves disagreement across labels. This enables the development of models that reason under uncertainty rather than relying solely on consensus.
-- **Limitations**: This dataset offers valuable insights into driver-pedestrian interactions and reasoning for autonomous driving research, but users should be aware of a few limitations: (1) Scale: the dataset is smaller than some large-scale benchmarks, which may affect generalization in data-hungry models. (2) Geographic scope: All videos were collected in U.S. cities, which may limit applicability to regions with different traffic rules, infrastructure, or pedestrian behavior. (3) Demographic representation: The dataset may not comprehensively represent diverse pedestrian demographics (e.g., age, mobility, clothing styles), which could lead to biased behavior prediction or reasoning outcomes. (4) Annotation Subjectivity: If the dataset includes reasoning or intention annotations, these may be subject to annotator bias or interpretation variability, especially in ambiguous scenarios. (5) The videos may not reflect the latest advancements in camera technology, changes in traffic behavior, vehicle technologies, or pdestrian norms, which could affect the relevance of the data for current autonomous driving systems.
-
+- **Bias and Representativeness**: Our group of 74 annotators includes 44 males and 30 females, ranging in age from 19 to 77. The age distribution is balanced across three groups — 19 to 30, 31 to 54, and 55 and older. All annotators hold valid U.S. driver’s licenses. Their driving experience varies, with approximately 25 percent driving around 5,000 miles per year, 40 percent driving about 10,000 miles per year, and the remainder exceeding 15,000 miles annually. This diversity provides a broad range of perspectives for pedestrian intent estimation and reasoning. However, we acknowledge that the dataset is U.S.-centric, as the driving scenes were collected from multiple cities across the United States, and may not generalize to global contexts.
+- **Privacy and Consent**: The dataset does not contain any personally identifiable information. All data was collected from public environments and anonymized where necessary. Users are responsible for ensuring compliance with local data protection regulations when using or sharing the dataset. All video and annotation data were reviewed and approved by the Indiana University legal department under an IRB-approved protocol. Users must agree to use the dataset solely for research purposes. While the current release does not include face or license plate blurring, we acknowledge this limitation and plan to implement de-identification and anonymization protocols in future updates.
+- **Social Impact and Ethical Considerations**: The dataset was collected with careful consideration of data ownership and consent. However, users should remain mindful of potential ethical concerns when deploying models trained on this data, particularly in sensitive domains such as surveillance, hiring, or healthcare. We recognize the risk of overfitting models to subjective human reasoning. To help mitigate this, PSI includes annotations from a diverse group of annotators and preserves disagreements across labels. This approach supports the development of models that can reason under uncertainty rather than relying solely on consensus.
+- **Limitations**: This dataset provides valuable insights into driver-pedestrian interactions and reasoning for autonomous driving research. However, users should be aware of several limitations. (1) The dataset is smaller than some large-scale benchmarks, which may affect generalization in data-intensive models. (2) Geographic scope — all videos were collected in U.S. cities, which may limit applicability to regions with different traffic rules, infrastructure, or pedestrian behavior. (3) Demographic representation — the dataset may not fully capture the diversity of pedestrian demographics, such as age, mobility, or clothing styles, which could introduce bias in behavior prediction or reasoning. (4) Annotation subjectivity — reasoning or intention annotations may reflect annotator bias or interpretation variability, especially in ambiguous scenarios. (5) Temporal relevance — the videos may not reflect recent advancements in camera technology, changes in traffic behavior, vehicle systems, or pedestrian norms, which could affect the dataset’s relevance for current autonomous driving systems.
 
 Researchers and developers are encouraged to consider these limitations when using the dataset and to complement it with additional data sources where possible.