MULTI-AGENT SYSTEM AND DATA ANALYTICS MULTI-AGENT SYSTEM AND DATA - - PowerPoint PPT Presentation

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MULTI-AGENT SYSTEM AND DATA ANALYTICS MULTI-AGENT SYSTEM AND DATA - - PowerPoint PPT Presentation

Oct 11, 2022 11 likes •132 views

SAFETY AND MOBILITY APPLICATION WITH SAFETY AND MOBILITY APPLICATION WITH MULTI-AGENT SYSTEM AND DATA ANALYTICS MULTI-AGENT SYSTEM AND DATA ANALYTICS Monty Abbas, Virginia Tech VT-SCORES (Qichao Wang and Awad Abdelhalim) Data Discovery for

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SAFETY AND MOBILITY APPLICATION WITH

MULTI-AGENT SYSTEM AND DATA ANALYTICS

SAFETY AND MOBILITY APPLICATION WITH

MULTI-AGENT SYSTEM AND DATA ANALYTICS

Monty Abbas, Virginia Tech VT-SCORES (Qichao Wang and Awad Abdelhalim) Data Discovery for November 16-17, 2017

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Background: Multi-agent System Background: Multi-agent System

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Goals System Agents

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Vision Vision

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NDS Varying Traffic Behavior Maneuvers Vehicle s Network Data (speed, acceleration, steering, etc.)

Straight travel, accelerate Straight travel, constant speed Straight travel, decelerate Steer, decelerate Steer, accelerate Other states Steer, constant speed Agent s learning process

Agents Library: Type and Frequency Safety Heat Map from Simulation Safety Heat Map from SmarterRoads Calibration

Simulation API

s s

Safety Simulation Kit

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Naturalistic driving behavior: event data Naturalistic driving behavior: event data

  • Training input: traffic states and actions
  • Training output: acceleration and steering
  • Input variables discretized using fuzzy sets
  • Continuous actions are generated from discrete

actions

  • Produces Individual agents combining acceleration

and steering behavior

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Learning Techniques Learning Techniques

  • Using actor-critic

Reinforcement Learning

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State 2 State 1 State 3 State 4 State 6 Other states State 5

State Action

State S Diagram Policy P

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Agent 1: Eta* Agent 1: Eta*

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Agent 2: Virginia* Agent 2: Virginia*

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Utilizing SmarterRoads Crash data Utilizing SmarterRoads Crash data

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The Matrix has you The Matrix has you

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Summary Summary

  • Possibilities and Impact
  • Develop a library of agents (including

disadvantages population)

  • Calibrate agent distribution in simulation to

replicate safety performance at a site

  • Evaluate potential improvement strategies

for public safety and mobility

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Summary Summary

  • Innovation
  • Combined car-following and lane changing

for normal and safety-critical driving

  • Better, more accurate modeling that can

accommodate disruptive technology (e.g., CAV)

  • Can replicate existing safety performance

(crashes) and mobility (congestion) in a region

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“…I don’t know the future. I didn’t come here to tell you how this is going to end; I came here to tell you how it is going to begin!”

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Thank you! Thank you!