In traditional LIDAR processing pipelines, a point-cloud is split into clusters, or objects, which are classified afterwards. This supposes that all the objects obtained by clustering belong to one of the classes that the classifier can recognize, which is hard to guarantee in practice. We thus propose an evidential end-to-end deep neural network to classify LIDAR objects. The system is capable of classifying ambiguous and incoherent objects as unknown, while only having been trained on vehicles and vulnerable road users. This is achieved thanks to an evidential reformulation of generalized logistic regression classifiers, and an online filtering strategy based on statistical assumptions. The training and testing were realized on LIDAR objects which were labelled in a semi-automatic fashion, and collected in different situations thanks to an autonomous driving and perception platform.

We propose an evidential fusion algorithm between LIDAR scans and RGB images. LIDAR points are classified as either belonging to the ground, or not, and RGB images are processed by a state-of-the-art convolutional neural network to obtain semantic labels. The results are fused into an evidential grid to assess the drivability of an area met by an autonomous vehicle, while accounting for incoherences over time and between sensors. The dynamic behaviour of potentially moving objects can be estimated from the high-level semantic labels. LIDAR scans and images are not assumed to be acquired at the same time, making the proposed grid mapping algorithm asynchronous. This approach is justified by the need for coping with, at the same time, sensor uncertainties, incoherences of results over time and between sensors, and the need for handling sensor failure. In classical LIDAR/camera fusion, in which LIDAR scans and images are considered to be acquired at the same time (synchronously), the failure of a single sensor leads to the failure of the whole fusion algorithm. On the contrary, the proposed asynchronous approach can be used to fuse contradictory information over time, while allowing the vehicle to operate even in the event of the failure of a single sensor. Experiments on a challenging use case highlight the interest of the method.

LIDAR sensors are usually used to provide autonomous vehicles with three‐dimensional representations of their environment. In ideal conditions, geometrical models could detect the road in LIDAR scans, at the cost of a manual tuning of numerical constraints, and a lack of flexibility. We instead propose an evidential pipeline, to accumulate road detection results obtained from neural networks. First, we introduce RoadSeg, a new convolutional architecture that is optimized for road detection in LIDAR scans. RoadSeg is used to classify individual LIDAR points as either belonging to the road, or not. Yet, such point‐level classification results need to be converted into a dense representation, that can be used by an autonomous vehicle. We thus second present an evidential road mapping algorithm, that fuses consecutive road detection results. We benefitted from a reinterpretation of logistic classifiers, which can be seen as generating a collection of simple evidential mass functions. An evidential grid map that depicts the road can then be obtained, by projecting the classification results from RoadSeg into grid cells, and by handling moving objects via conflict analysis. The system was trained and evaluated on real‐life data. A python implementation maintains a 10 Hz framerate. Since road labels were needed for training, a soft labeling procedure, relying lane‐level HD maps, was used to generate coarse training and validation sets. An additional test set was manually labeled for evaluation purposes. So as to reach satisfactory results, the system fuses road detection results obtained from three variants of RoadSeg, processing different LIDAR features.

LIDAR sensors are usually used to provide autonomous vehicles with 3D representations of their environment. In ideal conditions, geometrical models could detect the road in LIDAR scans, at the cost of a manual tuning of numerical constraints, and a lack of flexibility. We instead propose an evidential pipeline, to accumulate road detection results obtained from neural networks. First, we introduce RoadSeg, a new convolutional architecture that is optimized for road detection in LIDAR scans. RoadSeg is used to classify individual LIDAR points as either belonging to the road, or not. Yet, such point-level classification results need to be converted into a dense representation, that can be used by an autonomous vehicle. We thus secondly present an evidential road mapping algorithm, that fuses consecutive road detection results. We benefitted from a reinterpretation of logistic classifiers, which can be seen as generating a collection of simple evidential mass functions. An evidential grid map that depicts the road can then be obtained, by projecting the classification results from RoadSeg into grid cells, and by handling moving objects via conflict analysis. The system was trained and evaluated on real-life data. A python implementation maintains a 10 Hz framerate. Since road labels were needed for training, a soft labelling procedure, relying lane-level HD maps, was used to generate coarse training and validation sets. An additional test set was manually labelled for evaluation purposes. So as to reach satisfactory results, the system fuses road detection results obtained from three variants of RoadSeg, processing different LIDAR features.