Path tracking control for inverse problem of vehicle handling dynamics

A path tracking controller based on active disturbance rejection control(ADRC) theory is presented in this paper to solve path tracking problem in inverse vehicle handling dynamics. The basic idea behind the work is to design an active disturbance rejection controller according to yaw rate and lateral displacement during a vehicle travels along a prescribed path to generate an expected trajectory which guarantees minimum clearance to the prescribed path. Aiming at this purpose, using preview follower theory, a linear extended state observer based on lateral displacement is designed. Considering yaw angle of vehicle, a non-linear combination function combined error of lateral displacement as well as error of yaw angle is designed according to monotone bounded hyperbolic of tangent function. Finally, a real vehicle test is executed to verify the rationality of the path tracking controller. At the same time, according to characteristics of pavement file in Carsim, a 3-D virtual pavement model is established and ride comfort simulation of random pavement is carried out in the software model. The results show that the minimum lateral position error of the generated path tracking trajectory can be good indicators of successful solving of the path tracking problem in inverse vehicle handling dynamics for ADRC. More precisely, there is higher calculation accuracy for the algorithm of the ADRC to solve the path tracking problem. The study can help drivers easily identify safe lane-keeping trajectories and area

Optimal Energy Saving Adaptive Cruise Control in Overtaking Scenarios for a Hybrid Electric Vehicle

The overtaking planning problem plays a crucial role to foster the adaptive cruise control (ACC) technology. It reveals extremely challenging due to critical requirements on the real-time capability of the control system and on conflicting objectives for the longitudinal speed trajectory generated over time for the Following Vehicle (e.g. in terms of maneuver efficiency, passenger comfort, energy economy). In this paper, an approach to solve this problem is proposed by developing an optimal energy saving oriented ACC algorithm for overtaking scenarios considering a hybrid electric vehicle (HEV) as the Following Vehicle. An off-line optimization based on Dynamic Programming (DP) is implemented. The proposed DP formulation aims at controlling the Following Vehicle longitudinal jerk over time to minimize the overall HEV energy consumption throughout the overtaking maneuver. Optimization constraints are considered for the inter-vehicular distance between Leader Vehicle and Following vehicle over time, and for the operational limits of the HEV powertrain components. The developed ACC algorithm is demonstrated achieving up to 4.1% energy saving and significant improvements in terms of passenger comfort in different overtaking scenarios

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments

Behavioural parameters for CAVs

This document was created as part of the Levitate project. The purpose of this document is to define the Connected and Autonomous Vehicle (CAV) parameter sets for driving logics that are used in the Levitate project. The behaviour parameter sets are based on the microscopic traffic simulation software Aimsun Next (Aimsun, 2021). The assumptions on CAV parameters and their values were based on a comprehensive literature review, including both empirical and simulation-based studies (e.g., Cao et al., 2017; Eilbert et al., 2019; Goodall yet al., 2020; de Souza et al., 2021; Shladover et al., 2012), as well as discussions in meetings with experts, conducted as part of Levitate project