Connected autonomous vehicles are considered as mitigators
of issues such as traffic congestion, road safety, inefficient fuel
consumption and pollutant emissions that current road transportation
system suffers from. Connected autonomous vehicles
utilise communication systems to enhance the performance of
autonomous vehicles and consequently improve transportation by
enabling cooperative functionalities, namely, cooperative sensing
and cooperative manoeuvring. The former refers to the ability to
share and fuse information gathered from vehicle sensors and road
infrastructures to create a better understanding of the surrounding
environment while the latter enables groups of vehicles to drive in
a co-ordinated way which ultimately results in a safer and more efficient
driving environment. However, there is a gap in understanding
howand to what extent connectivity can contribute to improving the
efficiency, safety and performance of autonomous vehicles. Therefore,
the aim of this paper is to investigate the potential benefits
that can be achieved from connected autonomous vehicles through
analysing five use-cases: (i) vehicle platooning, (ii) lane changing,
(iii) intersection management, (iv) energy management and (v) road
friction estimation. The current paper highlights that although connectivity
can enhance the performance of autonomous vehicles and
contribute to the improvement of current transportation system performance,
the level of achievable benefits depends on factors such
as the penetration rate of connected vehicles, traffic scenarios and
the way of augmenting off-board information into vehicle control
systems.

Towards connected autonomous driving: review of use-cases

This paper deals with the trajectory following control problem of a class of autonomous vehicles with parametric uncertainties, external disturbances, and over-actuated features. A novel adaptive hierarchical control framework is proposed to supervise the lateral motion of autonomous four-wheel independent drive electric vehicles. First, an adaptive sliding mode high-level control law with the linear matrix inequality-based switching surface is designed to produce a vector of front steering angle and external yaw moment, in which the uncertain term and the switching control gain are adaptively regulated by the fuzzy logic technique, to further moderate the chattering phenomenon, an adaptive boundary layer is introduced. Second, a pseudo-inverse low-level control allocation algorithm is presented to optimally allocate the external yaw moment via coordinating and reconstructing the tire longitudinal forces. Finally, numerical simulation and experimental results demonstrate that the proposed adaptive control approach has outstanding tracking performance.

An Adaptive Hierarchical Trajectory Following Control Approach of Autonomous Four-Wheel Independent Drive Electric Vehicles

Estimation of tire-road friction coefficient based on combined APF-IEKF and iteration algorithm

Estimation of longitudinal force, lateral vehicle speed and yaw rate for four-wheel independent driven electric vehicles

In this paper, a review on road friction virtual sensing approaches is provided. In particular, this work attempts to address whether the road grip potential can be estimated accurately under regular driving conditions in which the vehicle responses remain within low longitudinal and lateral excitation levels. This review covers in detail the most relevant effect-based estimation methods; these are methods in which the road friction characteristics are inferred from the tyre responses: tyre slip, tyre vibration, and tyre noise. Slip-based approaches (longitudinal dynamics, lateral dynamics, and tyre self-alignment moment) are covered in the first part of the review, while low frequency and high frequency vibration-based works are presented in the following sections. Finally, a brief summary containing the main advantages and drawbacks derived from each estimation method and the future envisaged research lines are presented in the last sections of the paper.

Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

A new dynamic tire model for estimating the longitudinal/lateral road-tire friction force was derived in this paper. The model was based on the previous Dugoff tire model, in consideration of its drawback that it does not reflect the actual change trend that the tire friction force decreases with the increment of wheel slip ratio when it enters into the nonlinear region. The Dugoff model was modified by fitting a series of tire force data and compared with the commonly used Magic Formula model. This new dynamic friction model is able to capture accurately the transient behavior of the friction force observed during pure longitudinal wheel slip, lateral sideslip and combined slip situation. Simulation has been done under different situations, while the results validate the accuracy of the new tire friction model in predicting tire/road friction force during transient vehicle motion.

A Dynamic Model for Tire/Road Friction Estimation under Combined Longitudinal/Lateral Slip Situation

In this paper, a resonance frequency-based tire–road friction coefficient (TRFC) estimation method is proposed by considering the dynamics performance of the in-wheel motor drive system under small slip ratio conditions. A frequency response function (FRF) is deduced for the drive system that is composed of a dynamic tire model and a simplified motor model. A linear relationship between the squared system resonance frequency and the TFRC is described with the FRF. Furthermore, the resonance frequency is identified by the Auto-Regressive eXogenous model using the information of the motor torque and the wheel speed, and the TRFC is estimated thereafter by a recursive least squares filter with the identified resonance frequency. Finally, the effectiveness of the proposed approach is demonstrated through simulations and experimental tests on different road surfaces.

Tire–Road Friction Coefficient Estimation Based on the Resonance Frequency of In-Wheel Motor Drive System

Vehicle active safety systems can benefit significantly from a priori knowledge of the road conditions. This paper proposes a real-time tyre–road friction estimation algorithm based on an unscented Kalman filter and mean-square-error-weighted fusion, using measurements related to the electric vehicle and in-wheel motors. Because the modules are mutually independent, the dimensionality of the matrix used in the filtering process can be reduced to shorten the computational period. The approach can also work more effectively in various conditions than those techniques which focus on a specific direction of the vehicle dynamics. First, a modified Dugoff et al. tyre model is considered to express the non-linear characteristics of the tyres. Then, to observe the longitudinal tyre forces and the lateral tyre forces, a parameter identification technique that utilizes the information from an inertial sensor and a gyroscope is introduced. An unscented Kalman filter rather than the traditional linear filter is adopt...

Real-time identification of the tyre–road friction coefficient using an unscented Kalman filter and mean-square-error-weighted fusion:

Estimation of tire-road friction coefficient based on frequency domain data fusion

The invention discloses an electric vehicle travel planning method based on multi-target optimization, generally comprising the following steps: (1) a travel planning problem model is established; (2) drivers provide travel information; and (3) an optimal scheme is solved based on a timed multi-target ant colony optimization algorithm The problem model comprises a road network model, a vehicle model, and travel target and travel constraint definition Travel information includes: not providing any information, providing constraint information, and providing optimization goal and constraint information The ant colony optimization algorithm includes the steps of pheromone initialization, route transfer probability calculation, travel scheme search, air conditioner use determining, travel scheme ranking, pheromone updating, and loop optimization A dynamic stochastic road network model is used to describe the traffic environment and plan the travel of electric vehicles, and target characteristics corresponding to different travel schemes can be reflected The ant colony optimization algorithm ensures that a multi-target and multi-constraint optimized electric vehicle travel scheme is generated as the number of iterations increases

Chen Long

Papers

A Dynamic Model for Tire/Road Friction Estimation under Combined Longitudinal/Lateral Slip Situation

Tire–Road Friction Coefficient Estimation Based on the Resonance Frequency of In-Wheel Motor Drive System

Real-time identification of the tyre–road friction coefficient using an unscented Kalman filter and mean-square-error-weighted fusion:

Estimation of tire-road friction coefficient based on frequency domain data fusion

Electric vehicle travel planning method based on multi-target optimization