Predicting driver behavior is a key component for Advanced Driver Assistance Systems (ADAS). In this paper, a novel approach based on Support Vector Machine and Bayesian filtering is proposed for online lane change intention prediction. The approach uses the multiclass probabilistic outputs of the Support Vector Machine as an input to the Bayesian filter, and the output of the Bayesian filter is used for the final prediction of lane changes. A lane tracker integrated in a passenger vehicle is used for real-world data collection for the purpose of training and testing. Data from different drivers on different highways were used to evaluate the robustness of the approach. The results demonstrate that the proposed approach is able to predict driver intention to change lanes on average 1.3 seconds in advance, with a maximum prediction horizon of 3.29 seconds.

The article deals with the analysis and interpretation of dynamic scenes typical of urban driving. The key objective is to assess risks of collision for the ego-vehicle. We describe our concept and methods, which we have integrated and tested on our experimental platform on a Lexus car and a driving simulator. The on-board sensors deliver visual, telemetric and inertial data for environment monitoring. The sensor fusion uses our Bayesian Occupancy Filter for a spatio-temporal grid representation of the traffic scene. The underlying probabilistic approach is capable of dealing with uncertainties when modeling the environment as well as detecting and tracking dynamic objects. The collision risks are estimated as stochastic variables and are predicted for a short period ahead with the use of Hidden Markov Models and Gaussian processes. The software implementation takes advantage of our methods, which allow for parallel computation. Our tests have proven the relevance and feasibility of our approach for improving the safety of car driving.

Abstract-To be exploited for driving assistance purpose, a road obstacle detection system must have a good detection rate and an extremely low false detection rate. Moreover, the field of possible applications depends on the detection range of the system. With these ideas in mind, we propose in this paper a long range generic road obstacle detection system based on fusion between stereovision and laser scanner. The obstacles are detected and tracked by the laser sensor. Afterwards, stereovision is used to confirm the detections. An overview of the whole method is given. Then the confirmation process is detailed: three algorithms are proposed and compared on real road situations.

Abstract-Stereo-vision is extensively used for intelligent vehicles, mainly for obstacle detection, as it provides a large amount of data. Many authors use it as a classical 3D sensor which provides a large tri-dimensional cloud of metric measurements, and apply methods usually designed for other sensors, such as clustering based on a distance. For stereo-vision, the measurement uncertainty is related to the range. For medium to long range, often necessary in the field of intelligent vehicles, this uncertainty has a significant impact, limiting the use of this kind of approaches. On the other hand, some authors consider stereo-vision more like a vision sensor and choose to directly work in the disparity space. This provides the ability to exploit the connectivity of the measurements, but roughly takes into consideration the actual size of the objects. In this paper, we propose a probabilistic representation of the specific uncertainty for stereo-vision, which takes advantage of both aspects -distance and disparity. The model is presented and then applied to obstacle detection, using the occupancy grid framework. For this purpose, a computationally-efficient implementation based on the u-disparity approach is given.

The occupancy grid is a popular tool for probabilistic robotics, used for a variety of applications. Such grids are typically based on data from range sensors (e.g. laser, ultrasound), and the computation process is well known [1]. The use of stereo-vision in this framework is less common, and typically treats the stereo sensor as a distance sensor, or fails to account for the uncertainties specific to vision. In this paper, we propose a novel approach to compute occupancy grids from stereo-vision, for the purpose of intelligent vehicles. Occupancy is initially computed directly in the stereoscopic sensor's disparity space, using the sensor's pixel-wise precision during the computation process and allowing the handling of occlusions in the observed area. It is also computationally efficient, since it uses the u-disparity approach to avoid processing a large point cloud. In a second stage, this disparity-space occupancy is transformed into a Cartesian space occupancy grid to be used by subsequent applications. In this paper, we present the method and show results obtained with real road data, comparing this approach with others.

Abstract-Perception is a key component for any robotic system. In this paper we present a method to construct occupancy grids by fusing sensory information using Linear Opinion Pools. We used lidar sensors and a stereo-vision system mounted on a vehicle to make the experiments. To perform the validation, we compared the proposed method with the fusion method previously used in the Bayesian Occupancy Filter framework, using real data taken from highway and urban scenarios. The results show that our method is better at dealing with conflicting information coming from the sensors. We propose an implementation on parallel hardware which allows real-time execution.

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This chapter describes detection and tracking of moving objects (DATMO) for purposes of autonomous driving. DATMO provides awareness of road scene participants, which is important in order to make safe driving decisions and abide by the rules of the road. Three main classes of DATMO approaches are identified and discussed. First is the traditional approach, which includes data segmentation, data association, and filtering using primarily Kalman filters. Recent work within this class of approaches has focused on pattern recognition techniques. The second class is the model based approach, which performs inference directly on the sensor data without segmentation and association steps. This approach utilizes geometric object models and relies on non-parametric filters for inference. Finally, the third class is the grid based approach, which starts by constructing a low level grid representation of the dynamic environment. The resulting representation is immediately useful for determining free navigable space within the dynamic environment. Grid construction can be followed by segmentation, association, and filtering steps to provide object level representation of the scene. The chapter introduces main concepts, reviews relevant sensor technologies, and provides extensive references to recent work in the field. The chapter also provides a taxonomy of DATMO applications based on road scene environment and outlines requirements for each application.