Conception of a High-Level Perception and Localization System for Autonomous Driving

Abstract

This paper describes the conception of a high level, compact, scalable, and long autonomy perception and localization system for autonomous driving applications. Our benchmark is composed of a high resolution lidar (128 channels), a stereo global shutter camera, an inertial navigation system, a time server, and an embedded computer. In addition, in order to acquire data and build multi-modal datasets, this system embeds two perception algorithms (RBNN detection, DCNN detection) and one localization algorithm (lidar-based localization) to provide real-time advanced information such as object detection and localization in challenging environments (lack of GPS). In order to train and evaluate the perception algorithms, a dataset is built from 10.000 annotated lidar frames from various drives carried out under different weather conditions and different traffic and population densities. The performances of the three algorithms are competitive with the state-of-the-art. Moreover, the processing time of these algorithms are compatible with real-time autonomous driving applications. By providing directly accurate advanced outputs, this system might significantly facilitate the work of researchers and engineers with respect to planning and control modules. Thus, this study intends to contribute to democratizing access to autonomous vehicle research platforms.

Application of Machine Learning Models to the Analysis of Skid Resistance Data

“…, R N }. During the training process, the splitting process is guided by metrics such as Gini index Equation (10) and entropy Equation (11), which measure the impurity or the disorder of a set of samples.…”

“…Using data from various sensors and advanced algorithms, these systems interpret the vehicle's surrounding environment to alert the driver or to take control of the vehicle in order to mitigate potential hazards. While ADAS technologies based on object detection (vehicle, pedestrian, traffic signs, and others) have been extensively studied [7][8][9][10], the specific area concerning the interaction between the vehicle and road surface has received less interest. Among the reasons that might explain this observation, the following two points can be highlighted:…”

Localization for self-driving vehicles based on deep learning networks and RGB cameras

“…The device has various drawbacks, including potential issues in poor conditions and high computing requirements. These limits must be addressed to optimize the system's performance for autonomous driving applications in the actual world [32].…”

Experimental Validation: Perception and Localization Systems for Autonomous Vehicles using the Extended Kalman Filter Algorithm

“…One crucial component of the autonomous vehicle is its perception and localization system [4]. This system integrates sensors installed in the vehicle that assess its surroundings.…”