Abstract
Project Lead: Prof. Umit Ozguner
First mile (access to transportation choice) and last mile (from vehicular transportation termination to final destination) can be the weakest links in smart mobility. Many people in the US do not live or work close to a transportation access point, and many people are mobility impaired. Bridging this first mile / last mile gap in the transportation network requires operating in non-traditional environments that might be heavily populated by pedestrians. We have proposed a solution as a network of on demand automated vehicles and are initiating a program to test and demonstrate a selection of these within the Ohio State University main campus. In our preliminary studies we have determined that one of the key capabilities required of such a system is the ability to move in pedestrian dense environments- for example sidewalks, pedestrian malls and roads, business or academic campuses, intersections and crosswalks. This includes areas situations in which pedestrians do not always obey the rules or expectations. Therefore we are proposing a study of pedestrian motion in an environment where there are both static obstacles (trees, trash cans, lamp posts, fences, etc.) and moving platforms. We will model and simulate both individuals with different characteristics and dense crowds. We plan to also consider the guidance of such crowds, both for general traffic and for individual safety.
Year 1 of the project will involve model development and simulation for pedestrians and crowds. Data will be collected using experimental the platforms to support and validate the models. We will also explore behavior classification and the possibilities for modeling and achieving individual pedestrian and crowd guidance.
Year 2 will involve continued refinement and expansion of the year 1 models, along with simulation and planning for a potential deployment, considering first a route or area selected on the OSU campus.
Year 3 will establish a public data base of slow, slow vehicles going through pedestrian crowds. The data will be used locally to adjust parameters for crowd motion and in real-time path planning for the automated vehicles.
Description
First mile (access to transportation choice) and last mile (from vehicular transportation termination to final destination) can be the weakest links in smart mobility. Many people in the US do not live or work close to a transportation access point, and many people are mobility impaired. Bridging this first mile / last mile gap in the transportation network requires operating in non-traditional environments that might be heavily populated by pedestrians. We have proposed a solution as a network of on demand automated vehicles and are initiating a program to test and demonstrate a selection of these within the Ohio State University main campus. In our preliminary studies we have determined that one of the key capabilities required of such a system is the ability to move in pedestrian dense environments- for example sidewalks, pedestrian malls and roads, business or academic campuses, intersections and crosswalks. This includes areas situations in which pedestrians do not always obey the rules or expectations. Therefore we are proposing a study of pedestrian motion in an environment where there are both static obstacles (trees, trash cans, lamp posts, fences, etc.) and moving platforms. We will model and simulate both individuals with different characteristics and dense crowds. We plan to also consider the guidance of such crowds, both for general traffic and for individual safety.
Timeline
CMU Subcontract with OSU Signed: 6/27/2017
Year 1 of the project will involve model development and simulation for pedestrians and crowds. Data will be collected using experimental the platforms to support and validate the models. We will also explore behavior classification and the possibilities for modeling and achieving individual pedestrian and crowd guidance.
Year 2 will involve continued refinement and expansion of the year 1 models, along with simulation and planning for a potential deployment, considering first a route or area selected on the OSU campus.
Strategic Description / RD&T
Deployment Plan
Expected Outcomes/Impacts
Expected Outputs
TRID
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| hillstrom.7@osu.edu |
Hillstrom, Stacy |
The Ohio State University |
Other |
Other |
| ozguner.1@osu.edu |
Ozguner, Umit |
The Ohio State University |
PI |
Faculty - Tenured |
| redmill.1@osu.edu |
Redmill, Keith |
The Ohio State University |
Co-PI |
Other |
Budget
Amount of UTC Funds Awarded
$164323.00
Total Project Budget (from all funding sources)
$316587.00
Documents
| Type |
Name |
Uploaded |
| Publication |
“Smooth: improved short-distance mobility for a smarter city,” |
March 29, 2018, 9:39 a.m. |
| Publication |
“Agent-based microscopic pedestrian interaction with intelligent vehicles in shared space,” |
March 29, 2018, 9:39 a.m. |
| Presentation |
Keynote: Smart Cities: An Intelligent Vehicles Perspective |
March 29, 2018, 9:44 a.m. |
| Progress Report |
78_Progress_Report_2018-03-30 |
March 29, 2018, 9:45 a.m. |
| Publication |
social_force_in_IV18_-_not_appear_in_existing_documents.pdf |
Sept. 29, 2019, 7:57 p.m. |
| Progress Report |
78_Progress_Report_2018-09-30 |
Oct. 3, 2018, 10:07 a.m. |
| Project Brief |
Ozguner_2019_slides.pptx |
April 30, 2019, 9:30 a.m. |
| Project Brief |
Ozguner_2019_description.docx |
April 30, 2019, 9:29 a.m. |
| Data Management Plan |
dmp-Ozguner-2019.docx |
April 30, 2019, 9:33 a.m. |
| Publication |
dataset_IV_2019.pdf |
April 8, 2020, 1:39 p.m. |
| Publication |
mpc_social_force.pdf |
Sept. 29, 2019, 7:57 p.m. |
| Presentation |
Longitudinal Speed Regulation of Autonomous Vehicles Driving Through Pedestrian Crowd |
Sept. 29, 2019, 7:57 p.m. |
| Presentation |
Top-View Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus |
Sept. 29, 2019, 7:57 p.m. |
| Presentation |
Combining Social Force Model with Model Predictive Control for Vehicle's Longitudinal Speed Regulation in Pedestrian-Dense Scenarios |
Sept. 29, 2019, 7:57 p.m. |
| Publication |
A_Social_Force_Based.pdf |
April 8, 2020, 1:39 p.m. |
| Progress Report |
78_Progress_Report_2020-03-31 |
April 8, 2020, 1:42 p.m. |
| Progress Report |
78_Progress_Report_2020-09-30 |
May 1, 2020, 10:10 a.m. |
| Final Report |
PedestrianMotion-FinalReport-Project78.pdf |
May 1, 2020, 10:45 a.m. |
| Publication |
A Multi-State Social Force Based Framework for Vehicle-Pedestrian Interaction in Uncontrolled Pedestrian Crossing Scenarios |
Dec. 27, 2020, 11:01 p.m. |
| Publication |
Crowd motion detection and prediction for transportation efficiency in shared spaces |
Dec. 27, 2020, 11:02 p.m. |
| Publication |
A finite-sampling, operational domain specific, and provably unbiased connected and automated vehicle safety metric |
May 2, 2022, 9:43 a.m. |
| Publication |
Predicting pedestrian crossing intention with feature fusion and spatio-temporal attention |
May 2, 2022, 9:44 a.m. |
| Publication |
An Online Evolving Method For a Safe and Fast Automated Vehicle Control System |
May 2, 2022, 9:44 a.m. |
| Publication |
Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving |
May 2, 2022, 9:45 a.m. |
Match Sources
No match sources!
Partners
| Name |
Type |
| Dalian University of Technology |
None |
