This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Simulation Framework for Testing Autonomous Vehicles in a School for the Blind Campus
Technical Paper
2021-01-0118
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Event:
SAE WCX Digital Summit
Language:
English
Abstract
With the advent of increasing autonomous vehicles on public roads, the safety of vulnerable road users such as pedestrians, cyclists, etc. has never been more important. These especially include Blind or Visually Impaired (BVI) pedestrians who face difficulty in making confident decisions in road crossings without the help of accessible pedestrian signals (APS). This paper addresses some of the safety measures that can be taken to improve and assess the safety of BVI pedestrians in a controlled environment like a BVI school campus where autonomous vehicles are operated. The majority of research on autonomous vehicle safety does not consider the edge cases of encounters with BVI pedestrians. Based on this motivation, requirements and characteristics of Non-BVI and BVI pedestrians have been stated along with the motion models used to predict their future movements. Existing tools based on Bayesian multi-model filters were used for pedestrian tracking and motion predictions. These can be used for developing collision avoidance algorithms such as automatic emergency braking and evasive maneuvering. Path planning algorithms were developed and used to generate collision avoidance maneuvers. Extensive testing of the algorithms was carried out for multiple scenarios as defined by NHTSA’s pre-crash scenarios. For this purpose, a realistic virtual simulation environment was created using the CARLA Simulator. Other edge case scenarios such as distracted pedestrians, sensor latency, etc. were also tested using this simulation framework. The safety analysis assessment for the test scenarios was performed using pre-defined metrics.
Recommended Content
Authors
Topic
Citation
Kalidas, K., Gelbal, S., and Aksun Guvenc, B., "Simulation Framework for Testing Autonomous Vehicles in a School for the Blind Campus," SAE Technical Paper 2021-01-0118, 2021, https://doi.org/10.4271/2021-01-0118.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 |
Also In
References
- Li , X. , Arul Doss , A. , Aksun Guvenc , B. , and Guvenc , L. Pre-Deployment Testing of Low Speed, Urban Road Autonomous Driving in a Simulated Environment SAE Technical Paper 2020-01-0706 2020 https://doi.org/10.4271/2020-01-0706
- Emirler , M.T. , Uygan , I.M.C. , Aksun Guvenc , B. , and Guvenc , L. Robust PID Steering Control in Parameter Space for Highly Automated Driving International Journal of Vehicular Technology 2014 259465 2014
- Emirler , M.T. , Wang , H. , Aksun-Guvenc , B. , and Guvenc , L. Automated Robust Path Following Control based on Calculation of Lateral Deviation and Yaw Angle Error ASME Dynamic Systems and Control Conference, DSC 2015 Columbus, Ohio, U.S 2015
- Wang , H. , Tota , A. , Aksun-Guvenc , B. , and Guvenc , L. Real Time Implementation of Socially Acceptable Collision Avoidance of a Low Speed Autonomous Shuttle Using the Elastic Band Method IFAC Mechatronics Journal 50 April 2018 341 355 2018
- Zhou , H. , Jia , F. , Jing , H. , Liu , Z. , and Guvenc , L. Coordinated Longitudinal and Lateral Motion Control for Four Wheel Independent Motor-Drive Electric Vehicle IEEE Transactions on Vehicular Technology 67 5 3782 3379 2018
- Rasouli , A. , and Tsotsos , J.K. Autonomous Vehicles That Interact with Pedestrians: A Survey of Theory and Practice IEEE Transactions on Intelligent Transportation Systems 21 3 900 918 2020 10.1109/TITS.2019.2901817
- Administration, National Highway Traffic Safety NCSA Publications & Data Requests https://crashstats.nhtsa.dot.gov/ 2020
- Colley , M. , Walch , M. , Gugenheimer , J. , Askari , A. et al Towards Inclusive External Communication of Autonomous Vehicles for Pedestrians with Vision Impairments 2020 1 14
- Berrett , J.J. Pedestrian Walking Speeds at Signalized Intersections in Utah 2019 https://scholarsarchive.byu.edu/etd/7130
- Hogan , C. Analysis of Blind Pedestrian Deaths and Injuries From Motor Vehicle Crashes, 2002-2006 53 2019 10.1017/CBO9781107415324.004
- Ohio State School for the Blind https://ossb.ohio.gov/wps/portal/gov/ossb/ 2020
- Dosovitskiy , A. , Ros , G. , Codevilla , F. , Lopez , A. et al. CARLA: An Open Urban Driving Simulator 2017 http://arxiv.org/abs/1711.03938
- Epic Games Unreal Engine 4 https://www.unrealengine.com 2020
- Eliemichel/MapsModelsImporter https://github.com/eliemichel/MapsModelsImporter 2020
- Camara , F. , Bellotto , N. , Cosar , S. , Nathanael , D. et al. Pedestrian Models for Autonomous Driving Part I: Low Level Models, from Sensing to Tracking IEEE Transactions on Intelligent Transportation Systems X X n.d.
- Brouwer , N. Kloeden , H. , and Stiller , C. Comparison and Evaluation of Pedestrian Motion Models for Vehicle Safety Systems IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC 2207 12 2016 10.1109/ITSC.2016.7795912
- Camara , F. , Bellotto , N. , Cosar , S. , Weber , F. et al. Pedestrian Models for Autonomous Driving Part II: High Level Models of Human Behavior In Review X X 2019
- Rehder , E. , and Kloeden , H. Goal-Directed Pedestrian Prediction Proceedings of the IEEE International Conference on Computer Vision 2015 February 139 147 2015 10.1109/ICCVW.2015.28
- Kang , W. and Han , Y. A Simple and Realistic Pedestrian Model for Crowd Simulation and Application 1 6 2017 http://arxiv.org/abs/1708.03080
- GPS.gov GPS Accuracy https://www.gps.gov/systems/gps/performance/accuracy/#how-accurate 2020
- Genovese , A.F. The Interacting Multiple Model Algorithm for Accurate State Estimation of Maneuvering Targets Johns Hopkins APL Technical Digest (Applied Physics Laboratory) 22 4 614 623 2001
- Mazor , E. , Averbuch , A. , Bar-Shalom , Y. , and Dayan , J. Interacting Multiple Model Methods in Target Tracking: A Survey IEEE Transactions on Aerospace and Electronic Systems 34 1 103 123 Jan. 1998 10.1109/7.640267
- Plett , G.L. Multi-Target, Multi-Model Tracking ECE5550: Applied Kalman Filtering 1 24 2018
- Labbe , R.R. Jr. Kalman and Bayesian Filters in Python https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python 2020
- Nadarajah , N. , Tharmarasa , R. , McDonald , M. , and Kirubarajan , T. IMM Forward Filtering and Backward Smoothing for Maneuvering Target Tracking IEEE Transactions on Aerospace and Electronic Systems 48 3 2673 2678 July 2012 10.1109/TAES.2012.6237617
- Menzel , T. , Bagschik , G. , and Maurer , M. Scenarios for Development, Test and Validation of Automated Vehicles IEEE Intelligent Vehicles Symposium, Proceedings 2018 June 1821 1827 2018 10.1109/IVS.2018.8500406
- Menzel , T. , Bagschik , G. , Isensee , L. , Schomburg , A. et al. From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment IEEE Intelligent Vehicles Symposium, Proceedings 2019 June 2383 2390 2019 10.1109/IVS.2019.8814099
- Swanson , E.D. , Yanagisawa , M. , Najm , W. , Foderaro , F. et al. Crash Avoidance Needs and Countermeasure Profiles for Safety Applications Based on Light-Vehicle-to-Pedestrian Communications 2013
- ASAM OpenScenario https://www.asam.net/standards/detail/openscenario/ 2020
- ASAM OpenDrive https://www.asam.net/standards/detail/opendrive/ 2020
- CARLA Team Carla Scenario Runner https://github.com/carla-simulator/scenario_runner 2020
- Federal Highway Administration University Course on Bicycle and Pedestrian Transportation. Lesson 8: Pedestrian Characteristics 2006 https://www.fhwa.dot.gov/publications/research/safety/pedbike/05085/pdf/lesson8lo.pdf
- Poggenhans , F. , Pauls , J.H. , Janosovits , J. , Orf , S. et al Lanelet2: A High-Definition Map Framework for the Future of Automated Driving IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC 2018 Novemer 1672 79 2018 10.1109/ITSC.2018.8569929
- Jayaraman , S.K. , Robert , L.P. , Yang , X.J. et al. Efficient Behavior-Aware Control of Automated Vehicles at Crosswalks Using Minimal Information Pedestrian Prediction Model Proceedings of the American Control Conference 2020 July 4362 4368 2020 10.23919/ACC45564.2020.9147248