{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/diving-deeper-into-pedestrian-behavior","title":"Diving Deeper Into Pedestrian Behavior Understanding: Intention Estimation, Action Prediction, and Event Risk Assessment","arxiv_id":"2407.00446","date":"2024-06-29","proceeding":null,"authors":["Amir Rasouli","Iuliia Kotseruba"],"abstract":"In this paper, we delve into the pedestrian behavior understanding problem from the perspective of three different tasks: intention estimation, action prediction, and event risk assessment. We first define the tasks and discuss how these tasks are represented and annotated in two widely used pedestrian datasets, JAAD and PIE. We then propose a new benchmark based on these definitions, available annotations, and three new classes of metrics, each designed to assess different aspects of the model performance. We apply the new evaluation approach to examine four SOTA prediction models on each task and compare their performance w.r.t. metrics and input modalities. In particular, we analyze the differences between intention estimation and action prediction tasks by considering various scenarios and contextual factors. Lastly, we examine model agreement across these two tasks to show their complementary role. The proposed benchmark reveals new facts about the role of different data modalities, the tasks, and relevant data properties. We conclude by elaborating on our findings and proposing future research directions.","url_abs":"https://arxiv.org/abs/2407.00446v1","url_pdf":"https://arxiv.org/pdf/2407.00446v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"diving-deeper-into-pedestrian-behavior","repo_url":"https://github.com/aras62/PIE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2407.00446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.00446"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/aras62/PIE","reach":null}],"summary":{"ran_draft_wrong":4,"ran_fixture":1},"by_repo_kind":{"official":{"samples":5,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"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"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"3fcc2699b54fff2c","entry":"convert_to_imagewise_annt","repo":"aras62/PIE","repo_kind":"official","path":"visualization/model_visualize.py","file_url":"https://github.com/aras62/PIE/blob/HEAD/visualization/model_visualize.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3fcc2699b54fff2c"}},{"code_sha256_prefix":"7c407082f2b33af0","entry":"convert_to_pedwise_annt","repo":"aras62/PIE","repo_kind":"official","path":"visualization/model_visualize.py","file_url":"https://github.com/aras62/PIE/blob/HEAD/visualization/model_visualize.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7c407082f2b33af0"}},{"code_sha256_prefix":"20a648679f9c7c6f","entry":"get_base_metrics","repo":"aras62/PIE","repo_kind":"official","path":"scenarioEval/action_evaluate.py","file_url":"https://github.com/aras62/PIE/blob/HEAD/scenarioEval/action_evaluate.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"20a648679f9c7c6f"}},{"code_sha256_prefix":"cf86d1ee2b57344d","entry":"get_curve_metrics","repo":"aras62/PIE","repo_kind":"official","path":"scenarioEval/action_evaluate.py","file_url":"https://github.com/aras62/PIE/blob/HEAD/scenarioEval/action_evaluate.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cf86d1ee2b57344d"}},{"code_sha256_prefix":"7f7d06181a31a410","entry":"get_intention_labels","repo":"aras62/PIE","repo_kind":"official","path":"utilities/data_gen_utils.py","file_url":"https://github.com/aras62/PIE/blob/HEAD/utilities/data_gen_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7f7d06181a31a410"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}