https://blog.pouceheure.fr/tags/sensing_perception/Recent content in Sensing_perception on Hugo POUSSEURHugoenTue, 14 Oct 2025 00:00:00 +0000https://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/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/machine-learning-controller/Sun, 01 Jan 2023 00:00:00 +0000https://blog.pouceheure.fr/articles/machine-learning-controller/Abstract Researchers have been attempting to make the car drive autonomously. The environment perception together with safe guidance and control is an important task and are one of the big challenges when developing this kind of system. Geometrical or physical based models, machine learning based models and those based on a mixture of both models, are the three types of navigation methods used to resolve this problem. The last method takes advantage of the learning capability of machine learning models and uses the safeness of geometric models in order to better perform the navigation task.https://blog.pouceheure.fr/projects/friction-detection/Thu, 10 Nov 2022 00:00:00 +0000https://blog.pouceheure.fr/projects/friction-detection/Mission Context As part of a European research project, I contributed to a two-month research mission conducted at the University of Tokyo, focusing on road surface friction estimation using only a forward-facing camera. This work led to the publication of a paper, ProposalTakumi 2023 , at IEEE AIM 2023, in collaboration with the University of Tokyo and UTC. The system uses image segmentation, confidence modeling, geometric projection, and accumulation into a global surface grid map.https://blog.pouceheure.fr/projects/human-prediction/Thu, 10 Mar 2022 00:00:00 +0000https://blog.pouceheure.fr/projects/human-prediction/Abstract A multimodal deep learning model was developed to predict human driving behavior over a short time horizon. The training dataset was recorded specifically for this project. Video 1 - Video demo, human driving behavior prediction. More details about this work are provided in PredictionPousseur 2022 . Introduction Driving intention is represented as a sequence of vehicle states. Let $I$ be: $$ I = \{x_0, x_1, ..., x_n\} \quad \text{Eq (1)} $$ Here, $(x_i) = (v_i, w_i)$ denotes the state at time $i$, where $v_i$ is the linear velocity and $w_i$ is the angular velocity.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/projects/human-legs-detection/Thu, 10 Dec 2020 00:00:00 +0000https://blog.pouceheure.fr/projects/human-legs-detection/Overview Source Code In addition to the main GitHub project, the following submodules are available: Dataset of scanned legs (with labels): https://github.com/PouceHeure/dataset_lidar2D_legs Labeling GUI tool: https://github.com/PouceHeure/lidar_tool_label Radar interface: https://github.com/PouceHeure/ros_pygame_radar_2D Context This project detects human legs from 2D LiDAR scans using a Recurrent Neural Network (RNN) with LSTM cells. The LSTM processes each scan as a spatial sequence ordered by angle $ \theta $, not as a time series. The model learns local shape patterns in polar space that are typical of legs.