This work considers the cooperative trajectory-planning problem along a double lane change scenario for autonomous driving. In this paper, we develop two frameworks to solve this problem based on distributed model predictive control (MPC). The first approach solves a single nonlinear MPC problem. The general idea is to introduce a collision cost function in the optimization problem at the planning task to achieve a smooth and bounded collision function, and thus to prevent the need to implement tight hard constraints. The second method uses a hierarchical scheme with two main units: a trajectory-planning layer based on mixed-integer quadratic program (MIQP) computes an on-line collision-free trajectory using simplified motion dynamics, and a tracking controller unit to follow the trajectory from the higher level using the nonlinear vehicle model. Connected and automated vehicles (CAVs) sharing their planned trajectories lay the foundation of the cooperative behavior. In the tests and evaluation of the proposed methodologies, matlab-carsim cosimulation is utilized. carsim provides the high-fidelity model for the multibody vehicle dynamics. matlab-carsim conjoint simulation experiments compare both approaches for a cooperative double lane change maneuver of two vehicles moving along a one-way three-lane road with obstacles.

Besides the large amount of information multispectral imaging offers, multispectral visual odometry remains overlooked due to the dissimilarity between modalities. In order to tackle the challenging feature matching between multispectral stereo images and to overcome the lack of robust multispectral visual localisation solutions, a novel approach is proposed in this paper. It consists in tracking features in each modality simultaneously, in a monocular manner and then, estimating motion in a windowed bundle adjustment framework and using the geometry of the stereo setup to recover the missing scale. The estimation is robustified by selecting adequate keyframes based on feature parallax and by maximising the mutual information between all the features reprojected in the stereo pair. Furthermore, the proposed multispectral visual odometry solution is integrated in an error-state Kalman filter framework to deal with challenging environments, where the quality of images is reduced. Two measurements models, using absolute and relative camera poses, are presented. The superiority of relative poses is then shown by providing a failure recovery algorithm which relies on inertial data when visual data are not accessible. The algorithm was tested on innovative series of visible-thermal multispectral datasets, acquired from a car with real driving conditions. An overall error of around 2% of the travelled distance was achieved on these datasets.

This article presents a novel flight assistance system, obstacle avoidance system for teleoperation (OAST), whose main role is to make teleoperation of small multirotor unmanned aerial vehicles (UAVs) safer and more efficient in closed spaces. The OAST allows the operator to avoid obstacles while keeping a liberty of movement. The UAV is controlled through a 3-D haptic controller and the OAST amends the user input to increase safety and efficiency of the teleoperation. The design of the OAST is verified in computerized experiments. Moreover, a simulation involving 20 participants is carried out to validate the proposed scheme. This experiment shows that the OAST improves the completion time of the scenarios by 41% on average while reducing the workload of the operator from 57 to 27 points on the NASA Task Load Index test. The number of collisions with the environment is all but eliminated in these scenarios.

In this paper, vehicle cornering along roads with low friction coefficient is studied, and an autopilot design is proposed to satisfy desired handling performance. A novel hierarchical optimization approach is presented to generate off-line solutions for vehicle cornering problem along roads with different friction coefficients and radii of curvature. A vehicle status definition is introduced as a function of vehicle states that contains data to indicate handling performance. At each control instant, vehicle statuses are calculated, and at the end of each scenario, they are clustered with k-means clustering technique. The $vehicle status cluster centres$ are associated with the applied $control commands$. The pair formed by the vehicle status at cluster centre and the control command corresponds to a rule that produces the unique control command to be applied as a function of the vehicle status. The autopilot is constructed by a convex combination of these rules. This basic idea of autopilot design has been extended for motion along a specific rotation radius and friction coefficient; the control commands corresponding to arbitrary scenario parameters are obtained by a runtime scheduling of the weighted interpolation among the control commands corresponding to different scenario parameters. The proposed autopilot design is verified with vehicle motion under off-line optimized control commands. The autopilot is also tested along roads with varying friction coefficient, and it is shown that the vehicle satisfies the desired handling performance.

Research on visual odometry and localisation is largely dominated by solutions developed in the visible spectrum, where illumination is a critical factor. Other parts of the electromagnetic spectrum are currently being investigated to generate solutions dealing with extreme illumination conditions. Multispectral setups are particularly interesting as they provide information from different parts of the spectrum at once. However, the main challenge of such camera setups is the lack of similarity between the images produced, which makes conventional stereo matching techniques obsolete.This work investigates a new way of concurrently processing images from different spectra for application to visual odometry. It particularly focuses on the visible and Long Wave InfraRed (LWIR) spectral bands where dissimilarity between pixel intensities is maximal. A new Multimodal Monocular Visual Odometry solution (MMS-VO) is presented. With this novel approach, features are tracked simultaneously, but only the camera providing the best tracking quality is used to estimate motion. Visual odometry is performed within a windowed bundle adjustment framework, by alternating between the cameras as the nature of the scene changes. Furthermore, the motion estimation process is robustified by selecting adequate keyframes based on parallax.The algorithm was tested on a series of visible-thermal datasets, acquired from a car with real driving conditions. It is shown that feature tracking could be performed in both modalities with the same set of parameters. Additionally, the MMS-VO provides a superior visual odometry trajectory as one camera can compensate when the other is not working.