Analyzing the effects of geometric lane constraints on radar-based sensing of available vehicle headway using mapped lane geometry and camera registration of lane position

Krishna Varadarajan, Robert Leary, Evan Pelletier, Mohamed Wahba, Sean Brennan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Commercial trucks are currently equipped with a single front-facing RADAR mounted on the front bumper, as this is a sensor useful for Cooperative Adaptive Cruise Control, Emergency Braking Systems, and many similar Connected and Automated vehicle functions that require longitudinal vehicle control. This paper investigates the use of a bumper-mounted RADAR to perform traffic characterization around the ego-vehicle to obtain an estimate of the furthest headway that can be considered as a reliable estimate of open maneuvering space, such that there are no vehicles within the same lane as the ego-vehicle. This available headway in front of a vehicle is an important parameter in an ongoing study whose goal is to obtain improvements in fuel economy for highway driving of a tractor-trailer. But headway availability depends on the correct attribution of RADAR-measured vehicles to be either within the ego-lane, or outside the lane. The attribution of lane designations to specific RADAR targets depend strongly on lane geometry and the ability to align RADAR measurements to the ego-lane. This work investigates how knowledge of lane geometries, as well as sensor performance characteristics, may improve the trust in a RADAR measurement of open headway distance in front of a vehicle. Specifically, several strategies for associating local traffic either within or excluded from the ego lane are considered, and the possible sources of error in headway calculations are investigated for each strategy.

Original languageEnglish (US)
Title of host publicationAdvanced Driver Assistance and Autonomous Technologies; Advances in Control Design Methods; Advances in Robotics; Automotive Systems; Design, Modeling, Analysis, and Control of Assistive and Rehabilitation Devices; Diagnostics and Detection; Dynamics and Control of Human-Robot Systems; Energy Optimization for Intelligent Vehicle Systems; Estimation and Identification; Manufacturing
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791859148
DOIs
StatePublished - Jan 1 2019
EventASME 2019 Dynamic Systems and Control Conference, DSCC 2019 - Park City, United States
Duration: Oct 8 2019Oct 11 2019

Publication series

NameASME 2019 Dynamic Systems and Control Conference, DSCC 2019
Volume1

Conference

ConferenceASME 2019 Dynamic Systems and Control Conference, DSCC 2019
Country/TerritoryUnited States
CityPark City
Period10/8/1910/11/19

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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