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Conference Papers Year : 2022

Minimal Injury Risk Motion Planning using Active Mitigation and Sampling Model Predictive Control

Abstract

Collision mitigation is an important element in motion planning. Although Advanced Driver-Assistance Systems (ADAS) have a rich number of functionalities, they lack interchangeability. There is still a gap on finding a way to evaluate the best decision globally. This paper presents a novel motion planning framework to generate emergency maneuvers in complex and risky scenarios using active mitigation. The classical Model Predictive Path Integral (MPPI) algorithm is improved to be used in a probabilistic dynamic cost map under limited perception range. A cost map with global probability of injury to all road users is used as a constraint to the problem in order to compute target selection based on the global minimum risk considering all road users. Real experiments introduce the use of augmented sensor data by merging simulation and real sensor data to safely produce collision and mitigation experiments. Results show that the proposed algorithm can perform correctly in real time on board of the vehicle, by finding collision-free trajectories in complex scenarios and compute viable target selection that minimizes global injury risk when collision is inevitable.

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Dates and versions

hal-03812987 , version 1 (13-10-2022)

Identifiers

  • HAL Id : hal-03812987 , version 1

Cite

Luiz Alberto Serafim Guardini, Anne Spalanzani, Philippe Martinet, Christian Laugier, Thomas Genevois, et al.. Minimal Injury Risk Motion Planning using Active Mitigation and Sampling Model Predictive Control. ITSC 2022 - 25th IEEE Intelligent Transportation Systems Conference, Oct 2022, Macao, China. pp.1-6. ⟨hal-03812987⟩
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