An Integrated Estimation Scheme for Resolving Tire Deformation Problem of Autonomous Vehicles

Autonomous logistics vehicles are characterised by large changes in mass and their performances are greatly influenced by slope. In addition, sensors on autonomous vehicles are expensive and difficult to be installed considering application environment. To address these problems, a novel integrated estimation strategy for vehicle mass and road slope, which is based on the joint iteration of multi-model recursive least square (MMRLS) and Sage-Husa adaptive filter with the strong tracking filter (SH-STF), is proposed by utilising information involving speed, nominal engine torque and inherent parameters of vehicles. Firstly, due to the separate slowly-changing and time-dependent characteristics, the vehicle mass and road slope are estimated by using MMRLS and SH-STF separately. Secondly, the longitudinal dynamics gain and the steering dynamics gain are calculated separately based on each model’s residual probability distribution. Then, the two estimations module are combined by employing an iterative algorithm. Finally, the proposed strategy is verified by simulation and real vehicle tests. The tests result reveals that the estimation algorithm can effective estimate vehicle mass and road slope in real-time under straight going and steering conditions.

Download Full-text

Handling of tire pressure variation in autonomous vehicles: an integrated estimation and control design approach

2020 American Control Conference (ACC) ◽

10.23919/acc45564.2020.9147472 ◽

2020 ◽

Author(s):

Tamas Hegedus ◽

Daniel Fenyes ◽

Balazs Nemeth ◽

Peter Gaspar

Keyword(s):

Autonomous Vehicles ◽

Control Design ◽

Pressure Variation ◽

Design Approach ◽

Tire Pressure ◽

Estimation And Control ◽

And Control ◽

Integrated Estimation

Download Full-text

Development of a Predictive Collision Risk Estimation Scheme for Mixed Traffic

Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Systems; Biomedical/Bioengineering Applications; Building Energy Systems; Condition Based Monitoring; Control Design for Drilling Automation; Control of Ground Vehicles, Manipulators, Mechatronic Systems; Controls for Manufacturing; Distributed Control; Dynamic Modeling for Vehicle Systems; Dynamics and Control of Mobile and Locomotion Robots; Electrochemical Energy Systems ◽

10.1115/dscc2014-6144 ◽

2014 ◽

Cited By ~ 1

Author(s):

Je Hong Yoo ◽

Reza Langari

Keyword(s):

Autonomous Vehicles ◽

Controller Design ◽

Risk Estimation ◽

Autonomous Driving ◽

Collision Probability ◽

Subjective Perception ◽

Subsequent Work ◽

Prediction Ability ◽

Collision Model ◽

Estimation Scheme

Driven by the emergence of autonomous/semi-autonomous driving technologies, the mixed situation of autonomous vehicles and human drivers is of considerable significance. Toward this end, it is necessary to better understand human driving characteristics so as to predict the actions of the other cars. In this regard, we develop a basic framework for modeling driver behaviors in view of human prediction ability. Through the game theoretic estimation of the counterpart’s behaviors and the corresponding time-evolution of unsafe collision areas, we compute an objective collision model. In turn, we design a human-like predictive perception model on collision with an adjacent vehicle based on the objective collision model and the driver’s subjective level of safety assurance. Since drivers have different safety requirements, the subjective estimate on the collision was designed as a region in which has less safety than the driver’s own safety requirement in the objective probabilistic collision prediction. The region that is subjectively perceived based on the driver’s own safety standard is regarded as a deterministic unsafe region for the driver. That is to say, the subjective perception acts as a collision area with the collision probability of 1 so that the driver should avoid while driving. In our subsequent work, we will address the issue of controller design to avoid the subjective collision estimation.

Download Full-text