Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning

TL;DR

This paper introduces TVAL, an online torque vectoring method for active learning in autonomous emergency braking systems, improving safety by accurately estimating critical parameters with minimal driver disruption.

Contribution

The paper presents a novel online torque vectoring approach that estimates road friction and critical parameters while maintaining driver control and optimizing powertrain efficiency.

Findings

Successfully identifies road surface conditions in simulation

Maintains vehicle control with minimal driver disruption

Adapts to changing road surfaces using sensor-driven resampling

Abstract

Autonomous Emergency Braking (AEB) potentially brings significant improvements in automotive safety due to its ability to autonomously prevent collisions in situations where the driver may not be able to do so. Driven by the poor performance of the state of the art in recent testing, this work provides an online solution to identify critical parameters such as the current and maximum friction coefficients. The method introduced here, namely Torque Vectoring for Active Learning (TVAL), can perform state and parameter estimation whilst following the driver's input. Importantly with less power requirements than normal driving. Our method is designed with a crucial focus on ensuring minimal disruption to the driver, allowing them to maintain full control of the vehicle. Additionally, we exploit a rain/light sensor to drive the observer resampling to maintain estimation certainty across prolonged operation. Then a scheme to modulate TVAL is introduced that considers powertrain efficiency, safety, and availability in an online fashion. Using a high-fidelity vehicle model and drive cycle we demonstrate the functionality of TVAL controller across changing road surfaces where we successfully identify the road surface whenever possible.