Hybrid Sampling Bayesian Occupancy Filter

International audienceModeling and monitoring dynamic environments is a complex task but is crucial in the field of intelligent vehicle. A traditional way of addressing these issues is the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain. Instead of segmenting the scene into objects and track them, the environment is represented as a regular grid of occupancy, in which each cell is tracked at a sub-object level. The Bayesian Occupancy Filter is a generic occupancy grid framework which predicts the spread of spatial occupancy by estimating cell velocity distributions. However its velocity model, corresponding to a transition histogram per cell, leads to huge data management which in practice makes it hardly compatible to severe computational and hardware constraints, like in many embedded systems. In this paper, we present a new representation for the BOF, describing the environment through a mix of static and dynamic occupancy. This differentiation enables the use of a model adapted to the considered nature: static occupancy is described in a classic occupancy grid, while dynamic occupancy is modeled by a set of moving particles. Both static and dynamic parts are jointly generated and evaluated, their distribution over the cells being adjusted. This approach leads to a more compact model and to drastically improve the accuracy of the results, in particular in term of velocities. Experimental results show that the number of values required to model the velocities have been reduced from a typical 900 per cell (for a 30x30 neighborhood) to less than 2 per cell in average. The massive data compression allows to plan dedicated embedded devices

Embedded Bayesian Perception by Dynamic Occupancy Grid Filtering

International audienceA generic Bayesian perception framework, designed to estimate a dense representation of dynamic environments, by fusing and filtering multi-sensor data, has been developed, implemented and tested on embedded devices. The main features of the approach are the followings:• Data from multiple sensors are properly fused in probabilistic occupancy grids.• Motion and robust occupancy are estimated by a specific Bayesian filter.• Short-term collision risks and object parameters are assessed.• The whole system has been implemented and tested on Nvidia embedded devices, and produces real-time results

Hybrid Sampling Bayesian Occupancy Filter

International audienceModeling and monitoring dynamic environments is a complex task but is crucial in the field of intelligent vehicle. A traditional way of addressing these issues is the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain. Instead of segmenting the scene into objects and track them, the environment is represented as a regular grid of occupancy, in which each cell is tracked at a sub-object level. The Bayesian Occupancy Filter is a generic occupancy grid framework which predicts the spread of spatial occupancy by estimating cell velocity distributions. However its velocity model, corresponding to a transition histogram per cell, leads to huge data management which in practice makes it hardly compatible to severe computational and hardware constraints, like in many embedded systems. In this paper, we present a new representation for the BOF, describing the environment through a mix of static and dynamic occupancy. This differentiation enables the use of a model adapted to the considered nature: static occupancy is described in a classic occupancy grid, while dynamic occupancy is modeled by a set of moving particles. Both static and dynamic parts are jointly generated and evaluated, their distribution over the cells being adjusted. This approach leads to a more compact model and to drastically improve the accuracy of the results, in particular in term of velocities. Experimental results show that the number of values required to model the velocities have been reduced from a typical 900 per cell (for a 30x30 neighborhood) to less than 2 per cell in average. The massive data compression allows to plan dedicated embedded devices

Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps

Motion prediction is a challenging task for autonomous vehicles due to uncertainty in the sensor data, the non-deterministic nature of future, and complex behavior of agents. In this paper, we tackle this problem by representing the scene as dynamic occupancy grid maps (DOGMs), associating semantic labels to the occupied cells and incorporating map information. We propose a novel framework that combines deep-learning-based spatio-temporal and probabilistic approaches to predict vehicle behaviors.Contrary to the conventional OGM prediction methods, evaluation of our work is conducted against the ground truth annotations. We experiment and validate our results on real-world NuScenes dataset and show that our model shows superior ability to predict both static and dynamic vehicles compared to OGM predictions. Furthermore, we perform an ablation study and assess the role of semantic labels and map in the architecture.Comment: Accepted to the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

Probabilistic Grid-based Collision Risk Prediction for Driving Application

International audienceIn the recent years, more and more modern cars have been equipped with perception capabilities. One of the key applications of such perception systems is the estimation of a risk of collision. This is necessary for both Advanced Driver Assistance Systems and Autonomous Navigation. Most approach for risk estimation propose to detect and track the dynamic objects in the scene. Then the risk is estimated as a Time To Collision (TTC) by projecting the object's trajectory in the future. In this paper, we propose a new grid-based approach for collision risk prediction, based on the Hybrid-Sampling Bayesian Occupancy Filter framework. The idea is to compute an estimation of the TTC for each cell of the grid, instead of reasoning on objects. This strategy avoids to solve the difficult problem of multi-objects detection and tracking and provides a probabilistic estimation of the risk associated to each TTC value. After promising initial results, we propose in this paper to evaluate the relevance of the method for real on-road applications, by using a real-time implementation of our method in an experimental vehicle

Allo-centric Occupancy Grid Prediction for Urban Traffic Scene Using Video Prediction Networks

International audiencePrediction of dynamic environment is crucial to safe navigation of an autonomous vehicle. Urban traffic scenes are particularly challenging to forecast due to complex interactions between various dynamic agents, such as vehicles and vulnerable road users. Previous approaches have used egocentric occupancy grid maps to represent and predict dynamic environments. However, these predictions suffer from blurriness, loss of scene structure at turns, and vanishing of agents over longer prediction horizon. In this work, we propose a novel framework to make long-term predictions by representing the traffic scene in a fixed frame, referred as allo-centric occupancy grid. This allows for the static scene to remain fixed and to represent motion of the ego-vehicle on the grid like other agents'. We study the allo-centric grid prediction with different video prediction networks and validate the approach on the real-world Nuscenes dataset. The results demonstrate that the allo-centric grid representation significantly improves scene prediction, in comparison to the conventional ego-centric grid approach

A cross-prediction, hidden-state-augmented approach for Dynamic Occupancy Grid filtering

International audienceAccurate modeling of complex dynamic environments is a fundamental requirement in robotics and automotive applications. While grid-mapping approaches used to be limited to static settings, methods for dynamic occupancy grids have recently been developed, tracking spatial occupancy at a sub-object level, in every cell. In this paper, we present a generic dynamic occupancy grid tracker, which filters cell states andinfers dynamics of the scene through the interaction of a grid-based and a particle-based model. These are set to represent different parts of the scene, and optimize particle allocation only to relevant areas, their predictions being fused accordingly. New hidden variables in the filtering process permit to address previously mishandled situations, like concurrent state predictions or specific filtering sensitivity. The presented method has been implemented, optimized on a GPU and tested on real-road conditions, embedded on an experimental vehicle

Integration of ADAS algorithm in a Vehicle Prototype

International audienceFor several years, INRIA and Toyota Europe have been working together in the development of algorithms directed to ADAS. This paper will describe the main results of this successful joint project, applied to a prototype vehicle equipped with several sensors. This work will detail the framework, steps taken and motivation behind the developed technologies, as well as address the requirements needed for the automobile industry

Integration of ADAS algorithm in a Vehicle Prototype

International audienceFor several years, INRIA and Toyota Europe have been working together in the development of algorithms directed to ADAS. This paper will describe the main results of this successful joint project, applied to a prototype vehicle equipped with several sensors. This work will detail the framework, steps taken and motivation behind the developed technologies, as well as address the requirements needed for the automobile industry

Conditional Monte Carlo Dense Occupancy Tracker

International audienceProper modeling of dynamic environments is a core task in the field of intelligent vehicles. The most commonapproaches involve the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods.An alternative to a classic object model framework is the occupancy grid filtering domain. Instead of segmenting the sceneinto objects and track them, the environment is represented as a regular grid of occupancy, in which spatial occupancy istracked at a sub-object level. In this paper, we present the Conditional Monte Carlo Dense Occupancy Tracker, a genericspatial occupancy tracker, which infers dynamics of the scene through an hybrid representation of the environment, consistingof static occupancy, dynamic occupancy, empty spaces and unknown areas. This differentiation enables the use of state specificmodels (classic occupancy grid for motion-less components, set of moving particles for dynamic occupancy) as well as properconfidence estimation and management of data-less areas. The approach leads to a compact model that drastically improves theaccuracy of the results and the global efficiency in comparison to previous methods