Autonomous vehicle field of study has seen considerable researches within three decades. In the last decade particularly, interests in this field has undergone tremendous improvement. One of the main aspects in autonomous vehicle is the path tracking control, focusing on the vehicle control in lateral and longitudinal direction in order to follow a specified path or trajectory. In this paper, path tracking control is reviewed in terms of the basic vehicle model usually used; the control strategies usually employed in path tracking control, and the performance criteria used to evaluate the controller's performance. Vehicle model is categorised into several types depending on its linearity and the type of behaviour it simulates, while path tracking control is categorised depending on its approach. This paper provides critical review of each of these aspects in terms of its usage and disadvantages/advantages. Each aspect is summarised for better overall understanding. Based on the critical reviews, main challenges in the field of path tracking control is identified and future research direction is proposed. Several promising advancement is proposed with the main prospect is focused on adaptive geometric controller developed on a nonlinear vehicle model and tested with hardware-in-the-loop (HIL). It is hoped that this review can be treated as preliminary insight into the choice of controllers in path tracking control development for an autonomous ground vehicle.

Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges

Progress enables the creation of more automated and intelligent machines with increasing abilities that open up new roles between humans and machines. Only with a proper design for the resulting cooperative human–machine systems, these advances will make our lives easier, safer and enjoyable rather than harder and miserable. Starting from examples of natural cooperative systems, the paper investigates four cornerstone concepts for the design of such systems: ability, authority, control and responsibility, as well as their relationship to each other and to concepts like levels of automation and autonomy. Consistency in the relations between these concepts is identified as an important quality for the system design. A simple graphical tool is introduced that can help to visualize the cornerstone concepts and their relations in a single diagram. Examples from the automotive domain, where a cooperative guidance and control of highly automated vehicles is under investigation, demonstrate the application of the concepts and the tool. Transitions in authority and control, e.g. initiated by changes in the ability of human or machine, are identified as key challenges. A sufficient consistency of the mental models of human and machines, not only in the system use but also in the design and evaluation, can be a key enabler for a successful dynamic balance between humans and machines.

/pdf/towards-a-dynamic-balance-between-humans-and-automation-yn44l5aelv.pdf

Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations

Blimps are a promising platform for aerial robotics and have been studied extensively for this purpose. Unlike other aerial vehicles, blimps are relatively safe and also possess the ability to loiter for long periods. These advantages, however, have been difficult to exploit because blimp dynamics are complex and inherently non-linear. The classical approach to system modeling represents the system as an ordinary differential equation (ODE) based on Newtonian principles. A more recent modeling approach is based on representing state transitions as a Gaussian process (GP). In this paper, we present a general technique for system identification that combines these two modeling approaches into a single formulation. This is done by training a Gaussian process on the residual between the non-linear model and ground truth training data. The result is a GP-enhanced model that provides an estimate of uncertainty in addition to giving better state predictions than either ODE or GP alone. We show how the GP-enhanced model can be used in conjunction with reinforcement learning to generate a blimp controller that is superior to those learned with ODE or GP models alone.

Gaussian Processes and Reinforcement Learning for Identification and Control of an Autonomous Blimp

Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.

Research Advances and Challenges of Autonomous and Connected Ground Vehicles

The path-following problem for four-wheel independently actuated autonomous ground vehicles is investigated in this paper. A novel output constraint controller is proposed to deal with the lateral offset control in path following and maintain the vehicle lateral stability in the presence of tire sliding effects. The innovations of this work lie in the following two aspects: 1) A novel output constraint control strategy, namely, the hyperbolic projection method, is proposed to strictly bound the lateral offset to prevent the vehicle from transgressing the safety bound in path following; 2) an adaptive and robust linear quadratic regulator controller is adopted to obtain the optimal active front-wheel steering and direct yaw-moment control inputs with vehicle lateral stability consideration and to eliminate the effect of parameter uncertainties. CarSim–Simulink joint simulation results indicate that the proposed controller can compactly bound the lateral offset to avoid transgressing the safe boundary during path following, particularly in extreme driving conditions, in the presence of tire sliding effects and system uncertainties.

Output Constraint Control on Path Following of Four-Wheel Independently Actuated Autonomous Ground Vehicles

This paper describes a new motorcycle riding simulator whose purpose is twofold: 1) it can be used as a training tool for new riders in different scenarios, such as a normal traffic environments or in dangerous riding situations (avoidance, emergency braking, nearly failing or slipping situations, and bad weather conditions) and 2) it can be used to study motor cyclist behavior in such situations and rider-motorcycle interaction. Our studies have led to the development of an original five DOF mechanical platform including double haptic feedback on the handlebar. The remaining components are the basic movements consisting of pitch, roll, and yaw. These components are gathered in a parallel kinematics-type platform to enhance the movement bandwidth of the two-wheeled riding simulator. Despite its simplicity, the particular appeal of this simulator lies in the possibility of reproducing important motorcycle movements and inertial effects, which allow for the perception of sensations close to reality. The motivation behind the choice of platform movements and system actuation are described. Also, theoretical issues (modeling, identification, and control aspects) and performance results are provided.

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Mechatronics, Design, and Modeling of a Motorcycle Riding Simulator

This overview article describes the goals, concepts and very preliminary results of the subproject Joint System within the EU-project HAVEit. The goal of HAVEit is to develop and investigate vehicle automation beyond ADAS systems, especially highly automated driving, where the automation is doing a high percentage of the driving, while the driver is still meaningfully involved in the driving task. In HAVEit, an overarching architecture and several prototypes will be built up over time by manufacturers and suppliers. As a trail blazer, a Joint System prototype is under development by an interdisciplinary team of several European research institutes in order to investigate and demonstrate the basic principles of highly automated driving, which will then be gradually applied to vehicles closer to serial production. Starting with sensor data fusion, the Co-System part of the Joint Systems plans manoeuvres and trajectories, which are then used to control active interfaces and, taking into account the results of an online driver assessment, joined with the actions of the driver. While many aspects of this research undertaking are still under investigation, the concept, a first prototype and first results from a simulator evaluation will be sketched.

https://hal.inria.fr/inria-00533483/document

Towards Highly Automated Driving: Intermediate report on the HAVEit-Joint System

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac11-proceedings/data/html/papers/3594.pdf

Trajectory Tracking for Highly Automated Passenger Vehicles

Driving simulators are increasingly being used for driver evaluation and/or education. In this paper, we describe the design and the modeling aspects of a 2-degree-of-freedom (2-DOF) low-cost motion platform allowing the rendering of the longitudinal and yaw movements. This prototype will be used to study various configurations of motion rendering and the impact of these variants on controllability and simulator sickness. The whole motion platform is considered as two coupled systems that are linked mechanically. The first system consists of a motorized rail for the longitudinal movement, which is mounted on top of the second system, which is a motorized turret allowing rotation of the platform. We present the platform mechanics and a number of experimental studies that have been carried out to obtain a characterization of the platform capabilities and frequency responses, as well as to assess platform performance in a classical drive operation. First conclusions and directions of future work are presented.

/pdf/from-design-to-experiments-of-a-2-dof-vehicle-driving-44tudm765u.pdf

From Design to Experiments of a 2-DOF Vehicle Driving Simulator

This paper presents the various stages for the construction of a two wheeled riding simulator. Despite its simplicity, the particularity of this simulator comes from the possibility to reproduce most of the movements and the inertial effects allowing to perceive sensations close to reality cases. This simulator has been developed for two purposes: ldr as a training tool for new riders with different scenarios: normal traffic environment, dangerous riding situations (avoidance, emergency braking, nearly failling or slipping situations, bad weather conditions, etc.) ldr to study riders behaviours in such situations Our studies have lead to an original 5 degrees of freedom (DOF) mechanical platform including a double haptic feedback on the handlebar. The three basic movements are classical and consist of pitch, roll and yaw one. The choices of the platform movements and the system actuation are motivated and described. Also, some performances results are shown validating the initial requirements.

/pdf/design-and-modeling-of-a-new-motorcycle-riding-simulator-5e4x92ek94.pdf

Salim Hima

Papers

Mechatronics, Design, and Modeling of a Motorcycle Riding Simulator

Towards Highly Automated Driving: Intermediate report on the HAVEit-Joint System

Trajectory Tracking for Highly Automated Passenger Vehicles

From Design to Experiments of a 2-DOF Vehicle Driving Simulator

Design and Modeling of a New Motorcycle Riding Simulator