https://blog.pouceheure.fr/tags/autonomous_vehicle/Recent content in Autonomous_vehicle on Hugo POUSSEURHugoenMon, 10 Nov 2025 00:00:00 +0000https://blog.pouceheure.fr/projects/autosys-teleop/Mon, 10 Nov 2025 00:00:00 +0000https://blog.pouceheure.fr/projects/autosys-teleop/Demonstration Video Video 1 - Teleoperation Demo. (Train Demo) This Video 1: Teleoperation Demo. (Train Demo) illustrates the longitudinal control of the car via teleoperation. Example of speed saturation: 0:07 - 0:16 (limited by lateral acceleration) Example of braking: 1:20 (vehicle is stopped by teleop command) Context The vehicle autonomously plans a path and follows it. A teleoperator controls only the longitudinal speed using a joystick, similar to driving a train.https://blog.pouceheure.fr/projects/autosys-lidar-and-decision/Tue, 14 Oct 2025 00:00:00 +0000https://blog.pouceheure.fr/projects/autosys-lidar-and-decision/Demonstration Video 1 - Video demo of the LiDAR Detection pipeline Info: A video demonstration of the decision-making system is coming soon. Introduction In autonomous driving, the vehicle must be able to make a series of decisions, such as slowing down, stopping at an intersection, or waiting until the road is clear before proceeding. These decisions depend on the behavior of other road users. The system must therefore identify surrounding obstacles and determine which ones are relevant for decision-making.https://blog.pouceheure.fr/projects/autosys-traffic-light-detection/Thu, 15 May 2025 00:00:00 +0000https://blog.pouceheure.fr/projects/autosys-traffic-light-detection/Motivation As part of an autonomous driving project, I contributed to the development of a system enabling a vehicle to adapt its behavior to traffic lights. The vehicle adjusts its speed according to the current traffic light state. The project is divided into four main components: Matching: Identifying traffic light positions on the map Detection: Determining the current state of the traffic light Planning: Adjusting vehicle speed according to the detection result Control: Applying the computed velocity to the vehicle The detection pipeline is implemented in Python, while control is handled in C++ within a ROS2 environment.https://blog.pouceheure.fr/projects/autosys-control/Fri, 20 Dec 2024 00:00:00 +0000https://blog.pouceheure.fr/projects/autosys-control/Demonstration Video 1 - Demo video: planning and control in action. Overview As part of a collaborative autonomous driving project, I contributed to the low-level control system responsible for translating navigation commands into actuator signals (steering and torque). This system is fully integrated into a ROS2-based autonomous stack and has been deployed and tested on a real electric vehicle with in-wheel motors. Fig. 1 - Interfaces between planning and control modules.https://blog.pouceheure.fr/projects/autosys-local-planning/Tue, 10 Dec 2024 00:00:00 +0000https://blog.pouceheure.fr/projects/autosys-local-planning/Demonstration Video 1 - Demo video, pedestrian avoidance using lidar detection. Video 2 - Demo video, slalom test. Video 3 - Demo video, curve test. Overview As part of a collaborative autonomous driving project, I contributed to the implementation of the core planning functionalities for autonomous driving. The navigation stack transforms a target position into a safe and efficient motion trajectory using environmental context and map information. The process is divided into three stages:https://blog.pouceheure.fr/articles/thesis/Thu, 28 Mar 2024 00:00:00 +0000https://blog.pouceheure.fr/articles/thesis/Abstract My thesis is based on shared navigation between the autonomous system and the human. In our research, we focus on command fusion. In our approach, both entities, the human and the autonomous system, simultaneously control the vehicle, and a module acquires their commands and performs the command fusion. This approach involves studying the intentions of both the human and the autonomous system to ensure the most appropriate fusion of their choices and to evaluate the decision-making of each entity.https://blog.pouceheure.fr/projects/visual-control/Wed, 01 Feb 2023 00:00:00 +0000https://blog.pouceheure.fr/projects/visual-control/Abstract As part of a research project, I contributed to the development and testing of a Visual Servoing framework, a control strategy that links image feature variations to robot velocities. Applied to autonomous vehicles, this approach enables lane-keeping purely from camera input, without GPS or map dependency. It integrates lane detection using deep learning and transforms the output into features used in a control law that centers the vehicle in the lane.https://blog.pouceheure.fr/articles/general-multi-criteria/Sun, 01 Jan 2023 00:00:00 +0000https://blog.pouceheure.fr/articles/general-multi-criteria/Abstract 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.https://blog.pouceheure.fr/projects/lane-detection/Mon, 10 Jan 2022 00:00:00 +0000https://blog.pouceheure.fr/projects/lane-detection/Abstract Lane detection is an essential function for autonomous vehicles. While GPS and HD maps offer accurate localization under optimal conditions, vision-based detection provides an important backup, especially in areas with weak GNSS signals or outdated maps. This system implements a deep-learning lane detection pipeline using a convolutional autoencoder, designed to identify and segment multiple lane boundaries in various road conditions using only front-facing camera images. The approach integrates image preprocessing, deep-learning inference, optional temporal tracking, and curve fitting to generate stable and clean lane boundaries.https://blog.pouceheure.fr/articles/human-driving-prediction/Sat, 01 Jan 2022 00:00:00 +0000https://blog.pouceheure.fr/articles/human-driving-prediction/Abstract Predicting the intentions of the human and the machine on a near future is required to the human-machine shared control of automated intelligent vehicles. The autonomous system is able to inform about its future intentions, however it is not possible for the human to provide this information, it is necessary then to predict it. This paper proposes a deep learning methodology to predict human navigation intentions in a time horizon of a few seconds, using a recurrent neural network (RNN) architecture based on the Long Short-Term Memory (LSTM) architecture.https://blog.pouceheure.fr/articles/shared-decision/Fri, 01 Jan 2021 00:00:00 +0000https://blog.pouceheure.fr/articles/shared-decision/Abstract 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 by an autonomous system. The proposed shared decision-making model aims to improve the safety and efficiency of autonomous vehicle navigation by integrating human intuition and machine precision. Experimental results demonstrate the effectiveness of this approach in various driving scenarios. :contentReference[oaicite:0]{index=0}