The MIT - Cornell Collision and Why it Happened

Abstract

Mid-way through the 2007 DARPA Urban Challenge, MIT's autonomous Land Rover LR3 'Talos' and Team Cornell's autonomous Chevrolet Tahoe 'Skynet' collided in a low-speed accident, one of the first well-documented collisions between two full-size autonomous vehicles. This collaborative study between MIT and Cornell examines the root causes of the collision, which are identified in both teams' system designs. Systems-level descriptions of both autonomous vehicles are given, and additional detail is provided on sub-systems and algorithms implicated in the collision. A brief summary of robot-robot interactions during the race is presented, followed by an in-depth analysis of both robots' behaviors leading up to and during the Skynet-Talos collision. Data logs from the vehicles are used to show the gulf between autonomous and human-driven vehicle behavior at low speeds and close proximities. Contributing factors are shown to be: (1) difficulties in sensor data association leading to phantom obstacles and an inability to detect slow moving vehicles, (2) failure to anticipate vehicle intent, and (3) an over emphasis on lane constraints versus vehicle proximity in motion planning. Eye contact between human road users is a crucial communications channel for slow-moving close encounters between vehicles. Inter-vehicle communication may play a similar role for autonomous vehicles; however, there are availability and denial-of-service issues to be addressed.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2008
Accession Number
ADA520502

Entities

People

  • Aaron Nathan
  • Dan Huttenlocher
  • David Moore
  • Edwin Olson
  • Frank-robert Kline
  • Isaac Miller
  • John J. Leonard
  • Jonathan How
  • Luke Fletcher
  • Mark Campbell
  • Seth Teller
  • Yoshiaki Kuwata

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Autonomous Vehicles
  • Collision Avoidance
  • Collisions
  • Computer Science
  • Detection
  • Detectors
  • Estimators
  • Failure Mode And Effect Analysis
  • Geometry
  • Global Positioning Systems
  • Kalman Filters
  • Laser Rangefinding
  • Motion Planning
  • Navigation
  • Navigators
  • Unmanned Vehicles

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Systems Analysis and Design
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - DoD AI Strategy
  • Autonomy
  • Autonomy - Human-Robot Interaction