Abstract
The low-speed high automation (LSHA) is foreseen as a development path for new types of mobility, improving road safety and addressing transit problems in urban infrastructures. As these automation approaches are still in the development phase, methods to improve their design and validation are required. The use of vehicle simulation models allows reducing significantly the time deployment on real test tracks, which would not consider all the scenarios or complexity related to automated driving features. However, to ensure safety and accuracy while evaluating the proper operation of LSHA features, adequate validation methodologies are mandatory. In this study a two-step validation methodology is proposed: Firstly, an open-loop test set attempts to tune the required vehicle simulation models using experimental data considering also the dynamics of the actuation devices required for vehicle automation. Secondly, a closed-loop test strives to validate the selected automated driving functionality based on test plans, also improving the vehicle dynamics response. To illustrate the methodology, a study case is proposed using an automated Renault Twizy. In the first step, the brake pedal and steering wheel actuators’ behavior is modeled, as well as its longitudinal dynamics and turning capacity. Then, in a second step, an LSHA functionality for Traffic Jam Assist based on a Model Predictive Control approach is evaluated and validated. Results demonstrate that the proposed methodology is capable not only to tune vehicle simulation models for automated driving development purposes but also to validate LSHA functionalities.
Highlights
T HE development of driving automation functionalities has increased over the last decades, increasing their complexity while pushing to adequate the available validation procedures to this breakthrough technology [1]
A study case based on an automated Renault Twizy is detailed, in which a Traffic Jam Assist feature is implemented. This is achieved by a control formulation based on a Model Predictive Control (MPC) that makes use of an adaptive weight-related with lateral accelerations that avoid the use of a speed planner
Once a feasible dynamic model for the vehicle and its internal actuation system is defined, this model can be used to time-boost the development of ADAS/Automated Driving Systems (ADS) functionalities employing a simulation framework that permits the evaluation of perception, decision-making and/or motion control algorithms
Summary
T HE development of driving automation functionalities has increased over the last decades, increasing their complexity while pushing to adequate the available validation procedures to this breakthrough technology [1]. To make use of simulation-based validation approaches both lateral and longitudinal vehicle dynamics to have to be properly modeled and tuned, as in LSHA features both motion controls are required. A study case based on an automated Renault Twizy is detailed, in which a Traffic Jam Assist feature is implemented This is achieved by a control formulation based on a Model Predictive Control (MPC) that makes use of an adaptive weight-related with lateral accelerations that avoid the use of a speed planner.
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More From: IEEE Transactions on Intelligent Transportation Systems
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