Probability-Based Decision Making for Automated Highway Driving

Axel Niehaus; Axel Niehaus; Robert F. Stengel

doi:10.1109/25.312814

Probability-Based Decision Making for Automated Highway Driving

Axel Niehaus, Axel Niehaus, Robert F. Stengel

Mechanical & Aerospace Engineering

Research output: Contribution to journal › Article › peer-review

35 Scopus citations

Abstract

Real-time, rule-based guidance systems for autonomous vehicles on limited-access highways are investigated. The goal of these systems is to plan trajectories that are safe, while satisfying the driver's requests based on stochastic information about the vehicle state and the surrounding traffic. A rule-based system is used for high-level planning. Given a stochastic model of the traffic situation driven by current measurements, the probable evolution of traffic and the best trajectory to follow are predicted. Simulation results assess the impact of uncertain knowledge of traffic on the performance of the guidance system, showing that uncertainty can and must be taken into account.

Original language	English (US)
Pages (from-to)	626-634
Number of pages	9
Journal	IEEE Transactions on Vehicular Technology
Volume	43
Issue number	3
DOIs	https://doi.org/10.1109/25.312814
State	Published - Aug 1994

All Science Journal Classification (ASJC) codes

Aerospace Engineering
Electrical and Electronic Engineering
Computer Networks and Communications
Automotive Engineering

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10.1109/25.312814

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title = "Probability-Based Decision Making for Automated Highway Driving",

abstract = "Real-time, rule-based guidance systems for autonomous vehicles on limited-access highways are investigated. The goal of these systems is to plan trajectories that are safe, while satisfying the driver's requests based on stochastic information about the vehicle state and the surrounding traffic. A rule-based system is used for high-level planning. Given a stochastic model of the traffic situation driven by current measurements, the probable evolution of traffic and the best trajectory to follow are predicted. Simulation results assess the impact of uncertain knowledge of traffic on the performance of the guidance system, showing that uncertainty can and must be taken into account.",

author = "Axel Niehaus and Axel Niehaus and Stengel, {Robert F.}",

note = "Funding Information: Manuscript received May 20, 1992; revised March 12, 1993. This work was supported by grants from the U.S. Army Research Office and the Federal Highway Administration, with prior support by the General Motors Research Laboratories. This paper was presented at the International Conference on Vehicle Navigation and Information Systems, Dearborn, MI, October 1991. A. Niehaus was at the Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544 USA. He is now with Matra Automobile, Parc d{\textquoteright}Activit6s de Pissaloup, 78 191 Trappes Cedex, France. R. F. Stengel is with the Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544 USA. IEEE Log Number 9215108.",

year = "1994",

month = aug,

doi = "10.1109/25.312814",

language = "English (US)",

volume = "43",

pages = "626--634",

journal = "IEEE Transactions on Vehicular Technology",

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AU - Stengel, Robert F.

N1 - Funding Information: Manuscript received May 20, 1992; revised March 12, 1993. This work was supported by grants from the U.S. Army Research Office and the Federal Highway Administration, with prior support by the General Motors Research Laboratories. This paper was presented at the International Conference on Vehicle Navigation and Information Systems, Dearborn, MI, October 1991. A. Niehaus was at the Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544 USA. He is now with Matra Automobile, Parc d’Activit6s de Pissaloup, 78 191 Trappes Cedex, France. R. F. Stengel is with the Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544 USA. IEEE Log Number 9215108.

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N2 - Real-time, rule-based guidance systems for autonomous vehicles on limited-access highways are investigated. The goal of these systems is to plan trajectories that are safe, while satisfying the driver's requests based on stochastic information about the vehicle state and the surrounding traffic. A rule-based system is used for high-level planning. Given a stochastic model of the traffic situation driven by current measurements, the probable evolution of traffic and the best trajectory to follow are predicted. Simulation results assess the impact of uncertain knowledge of traffic on the performance of the guidance system, showing that uncertainty can and must be taken into account.

AB - Real-time, rule-based guidance systems for autonomous vehicles on limited-access highways are investigated. The goal of these systems is to plan trajectories that are safe, while satisfying the driver's requests based on stochastic information about the vehicle state and the surrounding traffic. A rule-based system is used for high-level planning. Given a stochastic model of the traffic situation driven by current measurements, the probable evolution of traffic and the best trajectory to follow are predicted. Simulation results assess the impact of uncertain knowledge of traffic on the performance of the guidance system, showing that uncertainty can and must be taken into account.

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Probability-Based Decision Making for Automated Highway Driving

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