Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarios

The abilities to understand the social interaction behaviors between a vehicle and its surroundings while predicting its trajectory in an urban environment are critical for road safety in autonomous driving. Social interactions are hard to explain because of their uncertainty. In recent years, neural network-based methods have been widely used for trajectory prediction and have been shown to outperform hand-crafted methods. However, these methods suffer from their lack of interpretability. In order to overcome this limitation, we combine the interpretability of a discrete choice model with the high accuracy of a neural network-based model for the task of vehicle trajectory prediction in an interactive environment. We implement and evaluate our model using the INTERACTION dataset and demonstrate the effectiveness of our proposed architecture to explain its predictions without compromising the accuracy.Comment: arXiv admin note: text overlap with arXiv:2105.03136 by other author

Participation of INRIA & Pl@ntNet to ImageCLEF 2011 plant images classification task

International audienceThis paper presents the participation of INRIA IMEDIA group and the Pl@ntNet project to ImageCLEF 2011 plant identification task. ImageCLEF's plant identification task provides a testbed for the system-oriented evaluation of tree species identification based on leaf images. The aim is to investigate image retrieval approaches in the context of crowdsourced images of leaves collected in a collaborative manner. IMEDIA submitted two runs to this task and obtained the best evaluation score for two of the three image categories addressed within the benchmark. The paper presents the two approaches employed, and provides an analysis of the obtained evaluation results

Combining Leaf Salient Points and Leaf Contour Descriptions for Plant Species Recognition

International audienceManual Plant identification done by experts is tedious and time consuming. This process needs to be automatic and easy to handle by the different stakeholders. In this paper, we propose an original method for plant species recognition, based on the leaf observation. We consider two sources of information: the leaf margin and the leaf salient points. For the leaf shape description, we investigate the shape context descriptor and two multiscale triangular approaches: the well-known triangle area representation (TAR) and the triangle side length representation (TSL). We propose then their combination with a shape-context based descriptor that represents the spatial correlation between the leaf salient points and the leaf margin. Experiments are carried out on three public leaf datasets. Results show that our approach achieves a high retrieval accuracy and outperforms state-of-art methods

Advanced shape context for plant species identification using leaf image retrieval

International audienceThis paper presents a novel method for leaf species identification combining local and shape-based features. Our approach extends the shape context model in two ways. First of all, two different sets of points are distinguished when computing the shape contexts: the voting set, i.e. the points used to describe the coarse arrangement of the shape and the computing set containing the points where the shape contexts are computed. This representation is enriched by introducing local features computed in the neighborhood of the computing points. Experiments show the effectiveness of our approach

A shape-based approach for leaf classification using multiscale triangular representation

International audienceIn this paper we introduce a new multiscale shape-based approach for leaf image retrieval. The leaf is represented by local descriptors associated with margin sample points. Within this local description, we study four multiscale triangle representations: the well known triangle area representation (TAR), the triangle side lengths representation (TSL) and two new representations that we denote triangle oriented angles (TOA) and triangle side lengths and angle representation (TSLA). Unlike existing TAR approaches, where a global matching is performed, the similarity measure is based on a locality sensitive hashing of local descriptors. The proposed approach is invariant under translation, rotation and scale and robust under partial occlusion. Evaluations made on four public leaf datasets show that our shape-based approach achieves a high retrieval accuracy w.r.t. state-of-art methods

Plant species recognition using spatial correlation between the leaf margin and the leaf salient points.

International audienceIn this paper, we propose an automatic approach for plant species identification, based on the visual information provided by the plant leaves. More precisely, we consider two sources of information: the leaf margin and the leaf salient points. We investigate two shape context based descriptors: the first one describes the leaf boundary while the second descriptor represents the spatial correlation between salient points of the leaf and its margin. We also study the performance of the fusion of these two descriptors on the ImageCLEF 2011 and 2012 leaf datasets. Experiments show the effectiveness and the efficiency of the proposed method

Non-local Social Pooling for Vehicle Trajectory Prediction

International audienceFor an efficient integration of autonomous vehicles on roads, human-like reasoning and decision making in complex traffic situations are needed. One of the key factors to achieve this goal is the estimation of the future behavior of the vehicles present in the scene. In this work, we propose a new approach to predict the motion of vehicles surrounding a target vehicle in a highway environment. Our approach is based on an LSTM encoder-decoder that uses a social pooling mechanism to model the interactions between all the neighboring vehicles. The originality of our social pooling module is that it combines both local and non-local operations. The non-local multi-head attention mechanism captures the relative importance of each vehicle despite the inter-vehicle distances to the target vehicle, while the local blocks represent nearby interactions between vehicles. This paper compares the proposed approach with the state-of-the-art using two naturalistic driving datasets: Next Generation Simulation (NGSIM) and the new highD Dataset. The proposed method outperforms existing ones in terms of RMS values of prediction error, which shows the effectiveness of combining local and non-local operations in such a context

Attention Based Vehicle Trajectory Prediction

International audienceSelf-driving vehicles need to continuously analyse the driving scene, understand the behavior of other road users and predict their future trajectories in order to plan a safe motion and reduce their reaction time. Motivated by this idea, this paper addresses the problem of vehicle trajectory prediction over an extended horizon. On highways, human drivers continuously adapt their speed and paths according to the behavior of their neighboring vehicles. Therefore, vehicles' trajectories are very correlated and considering vehicle interactions makes motion prediction possible even before the start of a clear maneuver pattern. To this end, we introduce and analyze trajectory prediction methods based on how they model the vehicles interactions. Inspired by human reasoning, we use an attention mechanism that explicitly highlights the importance of neighboring vehicles with respect to their future states. We go beyond pairwise vehicle interactions and model higher order interactions. Moreover, the existence of different goals and driving behaviors induces multiple potential futures. We exploit a combination of global and partial attention paid to surrounding vehicles to generate different possible trajectory. Experiments on highway datasets show that the proposed model outperforms the state-of-the-art performances

Relational Recurrent Neural Networks For Vehicle Trajectory Prediction

International audienceScene understanding and future motion prediction of surrounding vehicles are crucial to achieve safe and reliable decision-making and motion planning for autonomous driving in a highway environment. This is a challenging task considering the correlation between the drivers behaviors. Knowing the performance of Long Short Term Memories (LSTMs) in sequence modeling and the power of attention mechanism to capture long range dependencies, we bring relational recurrent neural networks (RRNNs) to tackle the vehicle motion prediction problem. We propose an RRNNs based encoder-decoder architecture where the encoder analyzes the patterns underlying in the past trajectories and the decoder generates the future trajectory sequence. The originality of this network is that it combines the advantages of the LSTM blocks in representing the temporal evolution of trajectories and the attention mechanism to model the relative interactions between vehicles. This paper compares the proposed approach with the LSTM encoder decoder using the new large scaled naturalistic driving highD dataset. The proposed method outperforms LSTM encoder decoder in terms of RMSE values of the predicted trajectories. It outputs an estimate of future trajectories over 5s time horizon for longitudinal and lateral prediction RMSE of about 3.34m and 0.48m, respectively

The ImageCLEF 2012 Plant Identification Task

International audienceThe ImageCLEF's plant identification task provides a testbed for the system-oriented evaluation of plant identification, more precisely on the 126 tree species identification based on leaf images. Three types of image content are considered: Scan, Scan-like (leaf photographs with a white uniform background), and Photograph (unconstrained leaf with natural background). The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a real-world application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of eleven groups from eight countries and with a total of 30 runs submitted, involving distinct and original methods, this second year pilot task confirms Image Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identification