Distinguishing obstacles from ground is an essential step for common perception tasks such as object detection-and-tracking or occupancy grid maps. Typical approaches rely on plane fitting or local geometric features, but their performance is reduced in situations with sloped terrain or sparse data. Some works address these issues using Markov Random Fields and Belief Propagation, but these rely on local geometric features uniquely. This article presents a strategy for ground segmentation in LiDAR point clouds composed by two main steps: (i) First, an initial classification is performed dividing the points in small groups and analyzing geometric features between them. (ii) Then, this initial classification is used to model the surrounding ground height as a Markov Random Field, which is solved using the Loopy Belief Propagation algorithm. Points are finally classified comparing their height with the estimated ground height map. On one hand, using an initial estimation to model the Markov Random Field provides a better description of the scene than local geometric features commonly used alone. On the other hand, using a graph-based approach with message passing achieves better results than simpler filtering or enhancement techniques, since data propagation compensates sparse distributions of LiDAR point clouds. Experiments are conducted with two different sources of information: nuScenes’s public dataset and an autonomous vehicle prototype. The estimation results are analyzed with respect to other methods, showing a good performance in a variety of situations.

Today, perception solutions for Automated Vehicles rely on sensors on board the vehicle, which are limited by the line of sight and occlusions caused by any other elements on the road. As an alternative, Vehicle-to-Everything (V2X) communications allow vehicles to cooperate and enhance their perception capabilities. Besides announcing its own presence and intentions, services such as Collective Perception (CPS) aim to share information about perceived objects as a high-level description. This work proposes a perception framework for fusing information from on-board sensors and data received via CPS messages (CPM). To that end, the environment is modeled using an occupancy grid where occupied, and free and uncertain space is considered. For each sensor, including V2X, independent grids are calculated from sensor measurements and uncertainties and then fused in terms of both occupancy and confidence. Moreover, the implementation of a Particle Filter allows the evolution of cell occupancy from one step to the next, allowing for object tracking. The proposed framework was validated on a set of experiments using real vehicles and infrastructure sensors for sensing static and dynamic objects. Results showed a good performance even under important uncertainties and delays, hence validating the viability of the proposed framework for Collective Perception.

LiDAR-based frameworks combining dynamic occupancy grids and object-level tracking are a popular approach for perception of the environment in autonomous driving applications. This paper presents a novel backchannel from the object-level module to the grid-level module that procures the enhancement of overall performance. This feedback leads to an enhanced grid representation by the inclusion of two new steps that allow semantic classification of the occupied space and the improvement of the dynamic estimation. To this end, objects extracted from the grid are analyzed with respect to potential object classes and displacement. Class likelihoods are filtered over time at cell-level using particles and a naive Bayesian classifier. The displacement information is computed taking into account semantic information and comparing objects in consecutive frames. Then, it is used to obtain velocity measurements that are used to enhance grid's dynamic estimation. In contrast to other approaches in the literature seeking similar objectives, this proposal does not rely on additional sensing technologies or neural networks. The evaluation is conducted with real sensor data in challenging urban scenarios.

Most advanced autonomous driving systems (ADS) today rely on the prior creation of high-definition maps (HD maps). This process is expensive and needs to be performed frequently to keep up with the changing conditions of the road environment. Creating accurate navigation maps online is an alternative to reduce the cost and broaden the current operational design domains (ODD) of modern ADS. This paper offers a snapshot of the state of the art in drivable area estimation, which is an essential technology to deploy ADS in ODDs where HD maps are limited or unavailable. The proposed review introduces a novel architecture breakdown that fits learning-based and non-learning-based techniques and allows the analysis of a set of impactful and recent drivable area algorithms. In addition to that, complimentary information for practitioners is provided: (i) an assessment of the influence of modern sensing technologies on the task under study and (ii) a selection of relevant datasets for evaluation and benchmarking purposes.

Dynamic Occupancy Grids are a popular approach for the estimation of the environment in autonomous driving applications. They offer several advantages, such as the capability to precisely address any type of obstacle and the estimation of the free and unknown space. Nevertheless, they lack contextual information and rely on a low-level representation format. Moreover, additional features beyond occupancy and dynamic states are required to achieve a comprehensive situation understanding. This work proposes an Evidential Semantic Lane-grid as a complementary environment representation to tackle these issues. First, the environment description of the Dynamic Occupancy Grid is extended by analyzing various factors that can influence the perception system's ability to estimate the surroundings and lead to model the unknown space. Then, this enhanced environment description is converted into a high-level representation format based on a semantic lane-grid. This format incorporates the context of the road layout and synthesizes the data with semantic labels, which are consistently estimated over time using an evidential filtering method that supports their meaning and prioritization criteria. Experimental results in real-world urban scenarios show the utility and feasibility of the proposed approach.