# Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis

arXiv: https://arxiv.org/abs/2305.04506 · HTML: https://mlsanity.com/paper/2305.04506/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 11 of 11)

- [Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans](https://mlsanity.com/paper/2001.00735/) (2020)
- [A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research](https://mlsanity.com/paper/2204.06527/) (2022)
- [3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams](https://mlsanity.com/paper/1905.00525/) (2019)
- [Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss](https://mlsanity.com/paper/2011.06679/) (2020)
- [Scene Compliant Trajectory Forecast with Agent-Centric Spatio-Temporal Grids](https://mlsanity.com/paper/1909.07507/) (2019)
- [Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over Full Visual Context](https://mlsanity.com/paper/2301.05804/) (2023)
- [On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset](https://mlsanity.com/paper/2112.00942/) (2021)
- [Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis](https://mlsanity.com/paper/1905.05350/) (2019)
- [Deep convolutional neural networks for pedestrian detection](https://mlsanity.com/paper/1510.03608/) (2015)
- [From Pedestrian Detection to Crosswalk Estimation: An EM Algorithm and Analysis on Diverse Datasets](https://mlsanity.com/paper/2205.12579/) (2022)
- [Pedestrian Emergency Braking in Ten Weeks](https://mlsanity.com/paper/2210.12115/) (2022)

## Built on by (most cited first, 9 of 9)

- [Language-Driven Active Learning for Diverse Open-Set 3D Object Detection](https://mlsanity.com/paper/2404.12856/) (2024)
- [doScenes: An Autonomous Driving Dataset with Natural Language Instruction for Human Interaction and Vision-Language Navigation](https://mlsanity.com/paper/2412.05893/) (2024)
- [Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving Safety](https://mlsanity.com/paper/2504.13399/) (2025)
- [Evaluating Vision-Language Models for Zero-Shot Detection, Classification, and Association of Motorcycles, Passengers, and Helmets](https://mlsanity.com/paper/2408.02244/) (2024)
- [Driver Activity Classification Using Generalizable Representations from Vision-Language Models](https://mlsanity.com/paper/2404.14906/) (2024)
- [Towards Vision Zero: The TUM Traffic Accid3nD Dataset](https://mlsanity.com/paper/2503.12095/) (2025)
- [Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors](https://mlsanity.com/paper/2502.00402/) (2025)
- [Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset](https://mlsanity.com/paper/2508.14567/) (2025)
- [Vision-Language Work Zone Intelligence for Safety-Critical Speed Regulation of Mixed-Autonomy Vehicles in Dynamic Environments](https://mlsanity.com/paper/2606.08860/) (2026)

## Abstract (arXiv)

It is critical for vehicles to prevent any collisions with pedestrians. Current methods for pedestrian collision prevention focus on integrating visual pedestrian detectors with Automatic Emergency Braking (AEB) systems which can trigger warnings and apply brakes as a pedestrian enters a vehicle's path. Unfortunately, pedestrian-detection-based systems can be hindered in certain situations such as night-time or when pedestrians are occluded. Our system addresses such issues using an online, map-based pedestrian detection aggregation system where common pedestrian locations are learned after repeated passes of locations. Using a carefully collected and annotated dataset in La Jolla, CA, we demonstrate the system's ability to learn pedestrian zones and generate advisory notices when a vehicle is approaching a pedestrian despite challenges like dark lighting or pedestrian occlusion. Using the number of correct advisories, false advisories, and missed advisories to define precision and recall performance metrics, we evaluate our system and discuss future positive effects with further data collection. We have made our code available at https://github.com/s7desai/ped-mapping, and a video demonstration of the CHAMP system at https://youtu.be/dxeCrS_Gpkw.