Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation

Predicting the trajectories of surrounding agents is an essential ability for autonomous vehicles navigating through complex traffic scenes. The future trajectories of agents can be inferred using two important cues: the locations and past motion of agents, and the static scene structure. Due to the high variability in scene structure and agent configurations, prior work has employed the attention mechanism, applied separately to the scene and agent configuration to learn the most salient parts of both cues. However, the two cues are tightly linked. The agent configuration can inform what part of the scene is most relevant to prediction. The static scene in turn can help determine the relative influence of agents on each other's motion. Moreover, the distribution of future trajectories is multimodal, with modes corresponding to the agent's intent. The agent's intent also informs what part of the scene and agent configuration is relevant to prediction. We thus propose a novel approach applying multi-head attention by considering a joint representation of the static scene and surrounding agents. We use each attention head to generate a distinct future trajectory to address multimodality of future trajectories. Our model achieves state of the art results on the nuScenes prediction benchmark and generates diverse future trajectories compliant with scene structure and agent configuration.Comment: Revised submission for RA-

Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation

https://arxiv.org/abs/2005.02545Predicting the trajectories of surrounding agents is an essential ability for autonomous vehicles navigating through complex traffic scenes. The future trajectories of agents can be inferred using two important cues: the locations and past motion of agents, and the static scene structure. Due to the high variability in scene structure and agent configurations, prior work has employed the attention mechanism, applied separately to the scene and agent configuration to learn the most salient parts of both cues. However, the two cues are tightly linked. The agent configuration can inform what part of the scene is most relevant to prediction. The static scene in turn can help determine the relative influence of agents on each others motion. Moreover, the distribution of future trajectories is multimodal, with modes corresponding to the agent's intent. The agent's intent also informs what part of the scene and agent configuration is relevant to prediction. We thus propose a novel approach applying multi-head attention by considering a joint representation of the static scene and surrounding agents. We use each attention head to generate a distinct future trajectory to address multimodality of future trajectories. Our model achieves state of the art results on the nuScenes prediction benchmark and generates diverse future trajectories compliant with scene structure and agent configuration

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

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

A Lightweight Goal-Based model for Trajectory Prediction

International audienceWe present a lightweight goal-based model for multimodal, probabilistic trajectory prediction for urban driving. Previous conditioned-on-goal methods have used map information in order to establish a set of potential goals and then complete the corresponding full trajectory for each goal. We instead propose two original representations, based on the agent's states and its kinematics, to extract the potential goals. In this paper, we conduct a comparative study between the two representations. We also evaluate our approach on the nuScenes dataset, and show that it outperforms a wide array of state-of-the-art methods

Prédiction de trajectoires pour les véhicules autonomes

The trajectory prediction of neighboring agents of an autonomous vehicle is essential for autonomous driving in order to perform trajectory planning in an efficient manner. In this thesis, we tackle the problem of predicting the trajectory of a target vehicle in two different environments; a highway and an urban area (intersection, roundabout, etc.). To this end, we develop solutions based on deep machine learning by phasing the interactions between the target vehicle and the static and dynamic elements of the scene. In addition, in order to take into account the uncertainty of the future, we generate multiple plausible trajectories and the probability of occurrence of each. We also make sure that the predicted trajectories are realistic and conform to the structure of the scene. The solutions developed are evaluated using real driving datasets.La prédiction de trajectoire des agents avoisinants d'un véhicule autonome est essentielle pour la conduite autonome afin d'effectuer une planification de trajectoire d'une manière efficace. Dans cette thèse, nous abordons la problématique de prédiction de trajectoire d'un véhicule cible dans deux environnements différents ; une autoroute et une zone urbaine (intersection, rond-point, etc.). Dans ce but, nous développons des solutions basées sur l'apprentissage automatique profond en mettant en phase les interactions entre le véhicule cibles et les éléments statiques et dynamiques de la scène. De plus, afin de tenir compte de l'incertitude du futur, nous générons de multiples trajectoires plausibles et la probabilité d'occurrence de chacune. Nous nous assurons également que les trajectoires prédites sont réalistes et conformes à la structure de la scène. Les solutions développées sont évaluées à à l'aide de bases de données de conduite réelles

Prédiction de trajectoires pour les véhicules autonomes

La prédiction de trajectoire des agents avoisinants d'un véhicule autonome est essentielle pour la conduite autonome afin d'effectuer une planification de trajectoire d'une manière efficace. Dans cette thèse, nous abordons la problématique de prédiction de trajectoire d'un véhicule cible dans deux environnements différents ; une autoroute et une zone urbaine (intersection, rond-point, etc.). Dans ce but, nous développons des solutions basées sur l'apprentissage automatique profond en mettant en phase les interactions entre le véhicule cibles et les éléments statiques et dynamiques de la scène. De plus, afin de tenir compte de l'incertitude du futur, nous générons de multiples trajectoires plausibles et la probabilité d'occurrence de chacune. Nous nous assurons également que les trajectoires prédites sont réalistes et conformes à la structure de la scène. Les solutions développées sont évaluées à à l'aide de bases de données de conduite réelles.The trajectory prediction of neighboring agents of an autonomous vehicle is essential for autonomous driving in order to perform trajectory planning in an efficient manner. In this thesis, we tackle the problem of predicting the trajectory of a target vehicle in two different environments; a highway and an urban area (intersection, roundabout, etc.). To this end, we develop solutions based on deep machine learning by phasing the interactions between the target vehicle and the static and dynamic elements of the scene. In addition, in order to take into account the uncertainty of the future, we generate multiple plausible trajectories and the probability of occurrence of each. We also make sure that the predicted trajectories are realistic and conform to the structure of the scene. The solutions developed are evaluated using real driving datasets

Prédiction de trajectoires pour les véhicules autonomes

The trajectory prediction of neighboring agents of an autonomous vehicle is essential for autonomous driving in order to perform trajectory planning in an efficient manner. In this thesis, we tackle the problem of predicting the trajectory of a target vehicle in two different environments; a highway and an urban area (intersection, roundabout, etc.). To this end, we develop solutions based on deep machine learning by phasing the interactions between the target vehicle and the static and dynamic elements of the scene. In addition, in order to take into account the uncertainty of the future, we generate multiple plausible trajectories and the probability of occurrence of each. We also make sure that the predicted trajectories are realistic and conform to the structure of the scene. The solutions developed are evaluated using real driving datasets.La prédiction de trajectoire des agents avoisinants d'un véhicule autonome est essentielle pour la conduite autonome afin d'effectuer une planification de trajectoire d'une manière efficace. Dans cette thèse, nous abordons la problématique de prédiction de trajectoire d'un véhicule cible dans deux environnements différents ; une autoroute et une zone urbaine (intersection, rond-point, etc.). Dans ce but, nous développons des solutions basées sur l'apprentissage automatique profond en mettant en phase les interactions entre le véhicule cibles et les éléments statiques et dynamiques de la scène. De plus, afin de tenir compte de l'incertitude du futur, nous générons de multiples trajectoires plausibles et la probabilité d'occurrence de chacune. Nous nous assurons également que les trajectoires prédites sont réalistes et conformes à la structure de la scène. Les solutions développées sont évaluées à à l'aide de bases de données de conduite réelles

Efficient online signature authentication approach

International audienceSignature authentication systems often have to focus their processing on acquired dynamic and/or static signatures descriptors to authenticate persons. This approach gives satisfactory results in ordinary cases but remains vulnerable against skilled forgeries. This is mainly because there is no relation between the signatory and his signature. We will show that the inclusion of the hand shape in the authentication process will considerably reduce the false acceptance rates of skilled forgeries and improve the authentication accuracy performances. A new online hand signature authentication approach based on both signature and hand shape descriptor is proposed. The signature acquisition is completely transparent, which allows a high level of security against fraudulent imitation attempts. Authentication performances are evaluated with extensive experiments. The obtained test results [equal  error  rate  (EER)=2%, genuine  acceptance  rate (GAR)=96%]confirm the efficiency of the proposed approach