Intelligent vehicle perception involves the correct detection and tracking of moving objects. Taking into account all the possible information at early levels of the perception task can improve the final model of the environment. In this paper, we present an evidential fusion framework to represent and combine evidence from multiple lists of sensor detections. Our fusion framework considers the position, shape and appearance information to represent, associate and combine sensor detections. Although our approach takes place at detection level, we propose a general architecture to include it as a part of a whole perception solution. Several experiments were conducted using real data from a vehicle demonstrator equipped with three main sensors: lidar, radar and camera. The obtained results show improvements regarding the reduction of false detections and mis-classifications of moving objects.

The paper addresses the problem of object perception for intelligent vehicle applications with main tasks of detection, tracking and classification of obstacles where multiple sensors (i.e.: lidar, camera and radar) are used. New algorithms for raw sensor data processing and sensor data fusion are introduced making the most information from all sensors in order to provide a more reliable and accurate information about objects in the vehicle environment. The proposed object perception module is implemented and tested on a demonstrator car in real-life traffics and evaluation results are presented.

In this paper, we detail a complete software architecture of a key task that an intelligent vehicle has to deal with: frontal object perception. This task is solved by processing raw data of a radar and a mono-camera to detect and track moving objects. Data sets obtained from highways, country roads and urban areas were used to test the proposed method. Several experiments were conducted to show that the proposed method obtains a better environment representation, i.e., reduces the false alarms and missed detections from individual sensor evidence.

In this paper, we present a generic architecture for perception of an intelligent vehicle in dynamic outdoor environment. This architecture is composed of two levels: a first level dedicated to real-time local simultaneous localization and mapping (SLAM) and a second one is dedicated to detection and tracking of moving objects (DATMO). The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm on a Daimler Mercedes demonstrator in the framework of the European Project PReVENT-ProFusion2 and on a Volkswagen Demonstrator in the framework of the European Project Intersafe2.

In this paper, we describe our approach for intersection safety developed in the scope of the European project INTERSAFE-2. A complete solution for the safety problem including the tasks of perception and risk assessment using on-board lidar and stereo-vision sensors will be presented and interesting results are shown.

Using multiple sensors in the context of environment perception for autonomous vehicles is quite common these days. Perceived data from these sensors can be fused at different levels like: before object detection, after object detection and finally after tracking the moving objects. In this paper we detail our object detection level fusion between laser and stereo vision sensors as opposed to pre-detection or track level fusion. We use the output of our laser processing to get a list of objects with position and dynamic properties for each object. Similarly we use the stereo vision output of another team which consists of a list of detected objects with position and classification properties for each object. We use Bayesian fusion technique on objects of these two lists to get a new list of fused objects. This fused list of objects is further used in tracking phase to track moving objects in an intersection like scenario. The results obtained on data sets of INTERSAFE-2 demonstrator vehicle show that this fusion has improved data association and track management steps.

We present a method of simultaneous detection and tracking moving objects from a moving vehicle equipped with a single layer laser scanner. A model-based approach is introduced to interpret the laser measurement sequence by hypotheses of moving object trajectories over a sliding window of time. Knowledge of various aspects including object model, measurement model, motion model are integrated in one theoretically sound Bayesian framework. The data-driven Markov chain Monte Carlo (DDMCMC) technique is used to sample the solution space effectively to find the optimal solution. Experiments and results on real-life data of urban traffic show promising results.

In this paper, we present a vehicle safety application based on data gathered by a laser scanner and two short-range radars that recognize unavoidable collisions with stationary objects before they take place to trigger restraint systems. Two different software modules that perform the processing of raw data and deliver a description of the vehicle's environment are compared. A comprehensive experimental evaluation based on relevant crash and noncrash scenarios is presented.

We describe a general architecture of vehicle perception system developed in the framework of the European project PReVENT-ProFusion. Our system consists of two main parts: the first part where the vehicle environment is mapped and moving objects are detected; and the second part where previously detected moving objects are verified and tracked. In this paper, we focus on the first part, using occupancy grid to model the vehicle environment, perform sensor data fusion and detect moving objects. Experimental results on a Volvo Truck equipped with laser scanner and radars show the effectiveness of our approach.

In this paper, we detail the hardware and software perception system designed and developed to track pedestrians using a set of offboard cameras. It has been used in the context of vulnerable safety in a car park. This architecture is divided in two parts: a fusion part to fusion the data given by the set of offboard cameras and a tracking part to sequentially estimate the position of each pedestrian present in the environment and to determine the number of pedestrians. Finally, some experimental results are presented