Determining the admissibility and quality of driving intentions, generated by an automated intelligent vehicle and by a human driver, is a sine qua none task in a human-intelligent vehicle share navigation. Our paper proposes a generic method to quantify driving intentions. These intentions are defined by a sequence of velocities. This formulation is based on metrics already discussed in the literature. It proposes a way to use them to evaluate a state, making a judgment, and to extend this evaluation to a sequence of states. This quantification determines whether the intention is safely achievable and defines a quality taking into account several criteria (safety, comfort, context, energy consumption). It is thus possible to compare and rank the intentions according to a set of criteria. This article defines a proposed implementation that has been tested on a driving simulator. For a given scenario, we tested our solution on several intentions in order to show the interest of our solution, and the possibility to compare the intentions between them.

Many studies faced the problem of vehicle autonomous navigation in different fields, but nowadays just a few of them uses all the implicit information coming from the context in which such navigation is occurring. This results in a huge potential information loss that prevents us from adapting the vehicle’s behavior to each different situation it may be in. In a previous work, we defined a method to model the static context of navigation using ontologies and take it into account in the command law when performing a local navigation task. In this paper, we extend our model of the context of navigation, and define a software architecture able to update the context dynamically, by using sensor information. The method is tested with real-time experiments on driving simulator. They show that the Context of Navigation can be effectively updated during the navigation and leads to a smarter vehicle’s behavior on the road.

This paper faces the problem of intelligent vehicles in interaction with their occupants and the environment, by modelling the semantic context associated to the navigation. With a semantically modelled context, an intelligent vehicle will not only drive itself safely, but it will also be able to reason on the situation and act accordingly. To do so, it is necessary to first define the Context of Navigation, and then to set the inference rules for it, in order to enrich the robot’s comprehension of the situation. In this paper we propose our definition of the Context of Navigation, based on the information that could be needed by the vehicle’s controller. We split it into two components: the Dynamic Context and the Static Context. In this paper we will focus on the latter. We then model the Static Context of Navigation of the autonomous driving - for instance the passengers’ driving preferences - and to make the robotic car adapt its behaviour to this new information in real time. Finally, a short practical example of our proposition is shown and discussed.

Shared control is a situation where humans and machines carry out a task in a cooperative way, and it is a popular approach to facilitate control and communication between humans and intelligent systems. Automated driving vehicles that share control with human drivers have been shown to acts better than fully automated systems and manual driving. However, there is little consensus in guidelines for the design and definition of shared control, some researchers assist humans using haptic feedback, and others use an optimization process and many other works. In this paper, a non-haptic shared control is applied to navigate an intelligent vehicle based on two inputs one by a human driver and the other obtained by an autonomous algorithm. Where, Non-Cooperative Game Theory is applied to formulate the final input of the vehicle in the form of linear velocity and steering wheel angle; where a joystick is used for manipulating human inputs and a computer vision technology “Visual Servoing” is implemented to command the autonomous driving inputs, after calculating the fusion coefficient value, which is determined by a function dependent on the admissibility of inputs and the similarity between them. The final navigation decision sent to the intelligent vehicle is the output of the fusion system. Noting that, it has the only right to command the driving inputs to the vehicle. Finally, simulation results are presented with the help of SCANeR Studio and MATLAB/SIMULINK Simulators.

Semi-active and active suspensions improves the maneuverability, comfort and stability of intelligent vehicles. Closed loop controllers for the active/semi-active suspension systems improve their performance as the uncertainty level decrease. In this article we present a methodology to estimate the main components of the suspension system with a single and integrated observer (virtual sensor). The proposed observer (an embedded algorithm) provides the system state, the road profile, the normal tire-ground forces and the load transfer without requiring expensive sensors. The sensors required are standard in commercial vehicles with actuated or semi-actuated suspension. The observer structure is based on Kalman filter technique due to its simplicity and robustness. The vehicle dynamics is fully integrated with the quarter-car suspension model in order to boost the system observability, its response and constraining the estimation error. The proposal is validated at different operation points using a high fidelity automotive simulator, OKTAL-SCANeR™studio-Callas® and experimental data.

Intelligent vehicles sense their dynamics and the environment to make proper decisions. Some of this information are hard to be measured or need expensive sensors. This paper addresses the estimation of road grade angles, along with tire-ground interaction forces, in a delayed interconnected cascade observer structure. A new approach using a Two-Stage Extended Kalman Filter is proposed, allowing a robust simultaneous estimation of the slow and fast dynamics variables. Experimental data is used to validate the estimator.

This paper presents a novel approach for estimation and prediction of vehicle dynamics states by incorporating digital road map and vehicle dynamics models. Precise information about vehicle dynamics states is essential for the safety and stability of vehicle. In particular, the tire-road contact forces and vehicle side slip angle are the most important parameters for evaluating the safety of vehicle. Nevertheless, these dynamics states are immeasurable with low cost sensors. Therefore, different observers, or the so-called virtual sensors are developed to estimate vehicle dynamics states. However, the existing observers are only capable in estimating vehicle dynamics states at a current instant but not to predict the potential dangers in a future instant. In order to make time for correcting drive behaviors, especially when driving at high speed, it seems very appealing for us to predict an impending dangerous event and react before the danger occurs. In this paper, the estimation of vehicle dynamics states is based on the fusion of information from inertial sensors, GPS and OpenStreetMap. The geometry of the upcoming path ahead of vehicle is provided by the digital map and is employed to predict the future dynamics states.

This paper presents a new hybrid control approach for vision-based navigation applied to autonomous robotic automobiles in urban environments. It is composed by a visual servoing (VS) for road lane following (as deliberative control) and a dynamic window approach (DWA) for obstacle avoidance (as reactive control). Typically, VS applications do not change the velocities to stop the robot in dangerous situations or avoid obstacles while performing the navigation task. However, in several urban conditions, these are elements that must be dealt with to guarantee the safe movement of the car. As a solution for this problem, in this paper, a line-following VS controller will be used to perform road lane following tasks with obstacle avoidance, validating its control outputs in a new image-based dynamic window approach (IDWA). The final solution combines the benefits of both controllers (VS+IDWA) for optimal lane following and fast obstacle avoidance, taking into account the car kinematics and some dynamics constraints. Experiments in a challenging scenario with both simulated and real experimental car show the viability of the proposed methodology.

This paper presents an approach for the development of Advanced Driving Assistance System (ADAS) based on the human-vehicle interaction using Image-based Dynamic Window Approach (IDWA). The IDWA is associated to a method for dynamic obstacles avoidance in order to prevent human driving errors, in the context of intelligent robotic vehicles. The human-vehicle interaction is presented by the correction of the Human Driving Behavior (HDB) controller for driving defaults of human drivers, with respect to referential paths that intimate the average driving path in real circumstances. The performance of the proposed human-vehicle interaction methodology, based on autonomous embedded functionalities, is simulated and verified in different bypass scenarios.

This paper presents the approach of an applicable safety driving methodology for human drivers based on Dynamic Window Approach (DWA), as an implementation of Advanced Driving Assist Systems (ADASs). The human driving behaviors are modelled for the design of controller, refined by referential paths using evasive trajectory model, and the linear and angular velocities are limited and corrected by DWA which performed as an obstacle avoidance strategy. Results of trajectory following and obstacle avoidance are compared with the Visual Servoing (VS) controller as a corresponding approach of autonomous control pattern.

The navigation of an autonomous vehicle is a highly complex task and the dynamic environment is used as a source for reasoning. Road detection is a major issue in autonomous systems and advanced driving assistance systems applied for inner-city. Uncertainty may arise in environments with unmarked or weakly marked roads or poor lightning conditions. Moreover, when a common benchmark is not used, it is hard to decide which approach performs better on the road detection problem. This paper introduces a comprehensive performance analysis of two road recognition approaches using the urban Kitti-road benchmark. The first approach makes the extraction of a feature set based on statistical measures of 2D and 3D information from each superpixel. An Artificial Neural Network is used to detect the road pattern. The second approach extracts the feature set based on a multi-normalized histogram of Textons and Disptons for each superpixel. This feature set is used as a source for a Joint Boosting algorithm to model the road pattern. The proposed work presents a detailed evaluation highliting the pros and cons of each approach.

Camera-based estimation of drivable image areas is still in evolution. These systems have been developed for improved safety and convenience, without the need to adapt itself to the environment. Machine Vision is an important tool to identify the region that includes the road in images. Road detection is the major task of autonomous vehicle guidance. In this way, this work proposes a drivable region detection algorithm that generates the region of interest from a dynamic threshold search method and from a drag process (DP). Applying the DP to estimation of drivable image areas has not been done yet, making the concept unique. Our system was has been evaluated from real data obtained by intelligent platforms and tested in different types of image texture, which include occlusion case, obstacle detection and reactive navigation.

This paper presents an approach for road detection based on image segmentation. This segmentation is resulted from merging 2D and 3D image processing data from a stereo vision system. The 2D layer returns a matrix containing pixel's clusters based on the Watershed transform. Whereas the 3D layer return labels, that are classified by the V-Disparity technique, to free spaces, obstacles and non-classified area. Thus, a feature's descriptor for each cluster is composed with features from both layers. The road pattern recognition was performed by an artificial neural network, trained to obtain a final result from this feature's descriptor. The proposed work reports real experiments carried out in a challenging urban environment to illustrate the validity and application of this approach.

This article leads to the challenging problem of increasing vehicle driving security by applying on boarded intelligent diagnosis systems; it presents a methodology of evaluating, in an anticipated way, the risk of having an accident (skid and rollover). The methodology consists in adopting assumptions about the trajectory, the longitudinal velocity and the longitudinal acceleration in future instants and use these assumptions, allied to previous road information to calculate the future vehicle dynamics parameters. Once calculated, the risk indicators based on these parameters could be predicted in order to expect and avoid possible dangerous situations. These indicators are the lateral load transfer (LTR) based on vertical forces, and the lateral skid indicator (LSI) based ont the maximum friction coefficient and the used friction coefficient. A sliding window system is used to apply the method on the whole trajectory to take into account the vehicle dynamics updates by the driver.

The active safety systems, such as Electronic Stability Program (ESP) and Traction Control System (TCS), have been used widely in the modern production auto-mobile. These safety control systems function well only when the vehicle motion information is known. To achieve this purpose, various sensors are equipped in the production cars in order to detect vehicle motion information like yaw, wheel velocities and accelerations. However some key variables of the vehicle dynamic and road conditions, such as the sideslip angle, the tire road forces and the tire road friction coefficient are difficult to measure directly for technology or economic reasons. A new observer which uses the unscented Kalman filter to estimate simultaneously the sideslip angle, lateral tire road forces and the tire road friction coefficient has been proposed in this paper. The advantage of this observer is that no additional sensors is needed when the ESP has been installed in the auto-mobile. The performance of the proposed observer is demonstrated through the Chicane test.

This paper presents a methodology for improving consistency of the simultaneous localization and mapping (SLAM) in large scale cyclic environments. The SLAM problem is embedded in a reactive sensor-based navigation approach and exploits data provided by a rotative laser range finder. The model of the unknown indoor environment is structured as an hybrid representation, both topological and metric, which is incrementally built during the exploration task. A global likelihood function is modeled from the geometric elastic relationships between different places of the environment, constrained by a rigid metric model inside each place. The inconsistencies in the final resulted map are minimized by deforming the hybrid model with the optimization of the global likelihood of the system by applying a relaxation methodology. Results are presented which shows the minimization of inconsistencies related to an autonomous constructed hybrid model by the application of the proposed methodology

This paper presents a methodology of error correction applied to autonomously mapping large scale cyclic environment by mobile robots based on laser scan measurements. The model of the unknown indoor environment is structured as a hybrid representation, both topological and geometrical, which is incrementally built during the exploration task. The correction methodology is based on the minimization of geometric elastic relations between different places of the environment, constrained by rigid geometrical model inside each place. Results are presented which shows the minimization of inconsistencies related to an autonomous constructed hybrid model by the application of the proposed methodology.

This paper presents a new methodology for large scale environment modelling by mobile robots. The model of the indoor environment is structured as an hybrid representation, both topological and geometrical, which is incrementally built during the exploration task. The topological aspect of the model captures the connectivity and accessibility of the different places in the environment, and the geometrical model supports a precise robot localization method and a map building of the free space. Experiments are shown which confirm the advantages of merging the topology and metrics of the environment in an hybrid model.

This paper presents a robust probabilistic approach based on the Bayesian estimation theory to extract and tracking line segment parameters from successive laser range scans, acquired during the evolution of a mobile robot in an indoor environment. In this methodology, a likelihood function is modelled and associated to the existence of structured objects around the robot, an uncertainty model associated to the telemetric data is derived and used to update the likelihood function. In this way, the distances to the near objects around the robot are estimated and used in the feedback loop of a sensor-based control navigation strategy. Experiments are performed using a mobile robot equiped with a 2D laser scanner device, validating the application of the Bayesian segmentation methodology in the laser-based robot navigation.

A pose estimation method applied to an indoor mobile robot equipped with a laser range-finder that periodically delivers a range scan of the environment is presented. The method is based on the fusion between the range readings delivered by the laser and the feedback control input computed to constrain the robot to move in its free workspace. The relative motion of the robot between two successive sampling times of the control loop is estimated:(1) computing a prediction by comparing the local maps built at these instants, (2) integrating the feedback control input computed from the laser readings to produce the desired motion and (3) performing a fusion between these two predictions. These computations are performed at sampling rate during the robot's evolution and are embedded in a probabilistic approach taking into account the uncertainties associated to the laser readings. Experimental results are presented validating the proposed method.

A bounded-error methodology applied to estimate relative displacements of an indoor mobile robot equipped with a laser range-finder that periodically delivers a range scan of the environment is presented. The method is based on the fusion between the range readings delivered by the laser and the feedback control input used to constraint the robot to move in its free workspace. A weighted least square estimation method, previously published (Victorino, Rives, and Borrelly, 2002), is recalled and a comparison between the proposed bounded-error methodology and the least square method is presented. Experimental results validate the application of the bounded-error estimation method.

This work presents a new methodology for navigating mobile robots in unknown environments based on the robot's perception. The interaction between the robot and the environment is modelled, and control laws are designed so that the robot is constrained to move on the Voronoi diagram of the environment. The proposed method enables the robot to explore an unknown scene without any reference trajectory or any prior knowledge about the environment and avoiding the obstacles. We apply this approach on an indoor mobile robot using a 2D laser range finder. We show that the displacement errors remain bounded when the movements of the robot are constrained by sensor based control laws, that results in a precise self-localization and reliable map building. Experimental results shown in this article validate this methodology.

Addresses the problem of simultaneous localization and map building for a mobile robot moving in an unknown environment. We show that the robustness of the techniques currently used can be notably enhanced when they are embedded in a sensor-based control scheme. We are developing such an approach applied to a navigation task of an indoor mobile robot using a 2-D laser range finder. The control objectives related to the sensor based control loop are defined thanks to the properties of the generalized Voronoi diagram. A roadmap of the environment is incrementally built from the laser range information. In this way the robot can explore an unknown scene without any reference trajectory or any prior knowledge about the environment. The localization and mapping process is performed in a probabilistic sense by fusing laser and odometry data thanks to an extended Kalman filter.