In the context of autonomous vehicles on highways, one of the first and most important tasks is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take into account the information from several sensors and fuse them with data coming from road maps. The localization problem on highways can be distilled into three main components. The first one consists of inferring on which road the vehicle is currently traveling. Indeed, Global Navigation Satellite Systems are not precise enough to deduce this information by themselves, and thus a filtering step is needed. The second component consists of estimating the vehicle’s position in its lane. Finally, the third and last one aims at assessing on which lane the vehicle is currently driving. These two last components are mandatory for safe driving as actions such as overtaking a vehicle require precise information about the current localization of the vehicle. In this survey, we introduce a taxonomy of the localization methods for autonomous vehicles in highway scenarios. We present each main component of the localization process, and discuss the advantages and drawbacks of the associated state-of-the-art methods.

In today's world, automatic navigation for a robotic device (autonomous vehicle and robot) is a pre-requisite for many complex tasks, which requires a robust localization method. We focus in this paper on the topic of localizing such a robot into an absolute and imprecise map. We propose a multi-sensor self-localization method, which is simultaneously able to operate with an imprecise map, as well as to improve the precision of an already existing one. The method uses split covariance intersection filter as well as an a priori selection of the best informative measurements out of all possible measurement sources at each time step. This selection scheme is based on an ``added Shannon information''-based criterion. We demonstrate in operation via statistical analysis the consistency of a refined map obtained from a biased map while keeping vehicle localization integrity. On top of this, we demonstrate solving of the so-called kidnapped-robot problem using the same framework.

Locating the vehicle in its road is a critical part of any autonomous vehicle system and has been subject to different research topics. In most of the works presented in the literature, ego-localization is split into three parts: Road level-localization: the road on which the vehicle travels, Lane level localization: the lane on which the vehicle travels, and Ego lane level localization: the lateral position of the vehicle in the ego-lane. For each part, several researches have been conducted. However, the relationship between the different parts has not been taken into consideration. Through this work, an end-to-end ego-localization framework is introduced with mainly two main novelties. The first one is that we propose a complete solution that tackles every parts of the ego-localization. The second one lies in the information-driven approach used. Indeed, we use prior about the road structure from a digital map in order to reduce the space complexity for the recognition process. Besides, several fusion framework techniques based on Bayesian Network and Hidden Markov Model are elaborated leading to an ego-localization method that is, to a large extent, robust to erroneous sensor data. The robustness of the proposed method is proven on different datasets in varying scenarios.

Evaluating the risk associated with operations is an essential element of safe planning and an essential prerequisite in mobile robotics. This issue is very broad, with numerous definitions emerging in the recent literature adapting different application scenarios and leading to different algorithmic approaches. In this review, we will investigate how the state-of-the-art approaches define the traversability risk, particularly for mobile robots, whereby we classify existing risk-aware navigation algorithms according to their characterization of risk. Subsequently, we will overview the formulations of risk assessment along a path using traversability grid maps since it is essential for a mobile robot to evaluate its path to predict potential hazards. Finally, we will discuss the consistency of commonly used risk metrics in robotics. The aim of the review is to offer a comprehensive overview to newcomers in the field, to provide a structured reference for practitioners, and to also inspire future developments.

In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited for computing risk assessments for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called the Lambda Field, which is specially designed for risk assessment. We first propose a way to compute such a map and the expectation of a generic risk over a path. Then, we demonstrate the benefits of our generic formulation with a use case defining the risk as the expected collision force over a path. Using this risk definition and the Lambda Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda Field gives a natural way to deal with unstructured environments, such as tall grass. Where standard environment representations would always generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken.

Robust perception in adverse weather conditions remains a significant challenge for autonomous vehicles. Short-wave infrared (SWIR) sensors offer specific physical properties that enable them to penetrate atmospheric disturbances like fog, rain, and snow. However, effectively combining this robustness with the textural and colour information of visible (VIS) cameras is difficult due to signal decorrelation and the limitations of static fusion schemes. To address this, we present VISWIR (Visible and SWIR Weighted Image Reconstruction), a pixel-level fusion method based on a multi-scale pyramid architecture. We introduce an automated strategy for scheduling parameters based on weather conditions using an optimisation framework. Rather than relying on static weights, our method applies offline parameter scheduling to adjust fusion hyperparameters based on the meteorological context. We focus on a multi-objective optimisation approach that maximises perceptual image quality via No-Reference Image Quality Assessment (NR-IQA) metrics. Validated in controlled environment scenarios with varying weather severities, our results confirm the potential of VISWIR as a robust, lightweight algorithmic baseline to enhance the perception capabilities of autonomous vehicles in adverse weather conditions.