A good path tracker is one of the keys for the successful development of a self-driving car. In the literature, there exists a wide variety of techniques, some complex and some simple and yet effective in particular scenarios. The choice of the path tracker influences the performance in terms of precision, stability and passenger comfort. This paper addresses the lateral control of a self-driving car in an urban environment, where speed is not high but variations in velocity and curvature are frequent. In choosing a lateral controller, simplicity, efficiency and robustness are considered as the main criteria. In this paper, three classical techniques used for controlling the lateral error are analyzed: pure pursuit, Stanley and a simplified kinematic steering control. Additionally, a novel kinematic controller based on the lateral speed is proposed. A home-made realistic simulation environment has been developed to allow rapid testing of the control laws. The relevance of this work has been demonstrated for all controllers through realistic simulations and experiments. The experimental site is the campus of Ecole Centrale de Nantes, where all control laws have been compared along the same path. A longer path, involving a portion of the ring road of Nantes (France) has been simulated. It involves speeds up to 90 km/h, allowing to extrapolate the comparison results to higher velocities.
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Self-driving car's navigation requires a very precise localization covering wide areas and long distances. Moreover, they have to do it at faster speeds than conventional mobile robots. This paper reports on an efficient technique to optimize the position of a sequence of maps along a journey. We take advantage of the short-term precision and reduced space on disk of the localization using 2D occupancy grid maps, from now on called sub-maps, as well as, the long-term global consistency of a Kalman filter that fuses odometry and GPS measurements. In our approach, horizontal planar LiDARs and odometry measurements are used to perform 2D-SLAM generating the sub-maps, and the EKF to generate the trajectory followed by the car in global coordinates. During the trip, after finishing each sub-map, a relaxation process is applied to a set of the last sub-maps to position them globally using both, global and map's local path. The importance of this method lies on its performance, expending low computing resources, so it can work in real time on a computer with conventional characteristics and on its robustness which makes it suitable for being used on a selfdriving car as it doesn't depend excessively on the availability of GPS signal or the eventual appearance of moving objects around the car. Extensive testing has been performed in the suburbs and in the downtown of Nantes (France) covering a distance of 25 kilometers with different traffic conditions obtaining satisfactory results for autonomous driving.
In this paper, a distributed observer-based approach is proposed to control the longitudinal motion of car-like vehicle platoon moving in an urban environment. To the best of our knowledge, this is the first work presenting an observer-based platoon controller that combines the advantages of high traffic capacity and a minimum number of communication links. To achieve a high traffic flow, a constant-spacing policy is used. However, for that policy, to make platoon string stable, the leader information must be broadcast to all the vehicles. Therefore, we propose a control law in which the predecessor position information is acquired by a sensor-based link while a communicationbased link is used to obtain the leader information. Then, an observer is designed and integrated into the control law such that the velocity information of the predecessor can be estimated without the need to communicate with the preceding vehicle. For navigation in urban environments, we present a third order platoon model represented in the curvilinear coordinates. Conditions for asymptotic stability and string stability are given considering the vehicle actuator dynamics and the induced network/sensor time delay. Finally, we provide both simulation and real-time results to validate our approach feasibility and to corroborate our theoretical findings. Index Terms-platoon in urban environments, curvilinear coordinates, observer-based longitudinal control, limited communication, high traffic flow.
Abstract. The availability of affordable RGB-D cameras which provide color and depth data at high data rates, such as Microsoft MS Kinect, poses a challenge to the limited resources of the computers onboard autonomous robots. Estimating the sensor trajectory, for example, is a key ingredient for robot localization and SLAM (Simultaneous Localization And Mapping), but current computers can hardly handle the stream of measurements. In this paper, we propose an efficient and reliable method to estimate the 6D movement of an RGB-D camera (3 linear translations and 3 rotation angles) of a moving RGB-D camera. Our approach is based on visual features that are mapped to the three Cartesian coordinates (3D) using measured depth. The features of consecutive frames are associated in 3D and the sensor pose increments are obtained by solving the resulting linear least square minimization system. The main contribution of our approach is the definition of a filter setup that produces the most reliable features that allows for keeping track of the sensor pose with a limited number of feature points. We systematically evaluate our approach using ground truth from an external measurement systems.
This paper proposes a distributed longitudinal controller for car-like vehicles platooning that travel in an urban environment. The presented control strategy combines the platoon maintaining, gap closure, and collision avoidance functionality into a unified control law. A consensus-based controller designed in the path coordinates is the basis of the proposed control strategy and its role is to achieve position and velocity consensus among the platoon members taking into consideration the nature of the motion in an urban environment. For platoon creation, gap closure scenario is highly recommended for achieving a fast convergence of the platoon. For that, an algorithm is proposed to adjust the controller parameters online. A longitudinal collision between followers can occur due to several circumstances. Therefore, the proposed control strategy considers the assurance of collision avoidance by the guarantee of a minimum safe inter-vehicle distance. Convergence of the proposed algorithm is proved in the different modes of operations. Finally, studies are conducted to demonstrate and validate the efficiency of the proposed control strategy under different driving conditions. To better emulate a realistic setup, the controller is tested by an implementation of the car-like vehicles platoon in a vehicular mobility simulator called ICARS, which considers the real vehicle dynamics and other platooning staff in urban environments.
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