Automotive Electrical/Electronic (E/E)-architectures consist of various components which are generally developed independently. Due to the increasing size and complexity, component integration is highly challenging and already slight modifications to components or subsystems often require expensive re-testing and re-validation. As a remedy, we propose a framework for modular architectures based on a data-centric description and a fully time-triggered scheduling. This modular design approach is enabled by a novel methodology for schedule integration where local schedules are defined independently for subsystems before being integrated into a global schedule. This divide-and-conquer approach significantly reduces the integration complexity while the system becomes highly composable. Our experimental results give evidence of the efficiency and versatility of the proposed approach, using networks based on a time-triggered automotive Ethernet.

This paper presents a new multiple vehicle cooperative localization approach based on Random Finite Set (RFS) theory. Assuming vehicles are equipped with proprioceptive and exteroceptive sensors to localize the positions, a solution based on RFS statistics is therefore proposed to consider the whole group behavior instead of each vehicle. For this, we rely on Probability Hypothesis Density (PHD) filtering. Compared to other methods, our approach presents a recursive filtering algorithm that provides dynamic estimation of multiple vehicle states. The proposed method addresses the current challenges in multiple vehicle cooperative localization domain such as communication bandwidth issue, data association uncertainty and the over-convergence problem. A comparative study based on simulations demonstrates the reliability and the feasibility of the proposed approach in large scale environments.

Within the past few years, lane detection technology has become of high interest in the field of intelligent vehicles; however, robustness is still an issue. The challenge is to extract the lane markings effectively from the complex urban environment. In this paper, we present a novel approach based on Random Finite Set Statistics for estimating the position of lane markings. We rely on Probability Hypothesis Density (PHD) filtering and apply this technique to lane marking extraction in urban environment. Our method is based on two phases: an image preprocessing phase to extract pixels that potentially represent lanes and a tracking phase to identify lane markings. Compared to other approaches, our method presents a recursive filtering algorithm which extracts lane markings in the presence of clutter and non-lane markings. The experimental results exhibit the high performance of the proposed approach under various scenarios.

This paper presents a novel approach based on Random Finite Set (RFS) Statistics for estimating a vehicle's trajectory in complex urban environments by using a fixed single camera. For this, we extend our earlier works which used Probability Hypothesis Density (PHD) filtering under sensor fusion framework and are among the first to apply this technique to visual odometry in real traffic scenes. We consider features acquired from the camera as a group targets, use the PHD filter to update the overall group state and then estimate the ego-motion vector of the camera. Compared to other approaches, our approach presents a recursive filtering algorithm that provides dynamic estimation of multiple-targets states in the presence of clutter and avoids the association problem. Experimental results show that this method provides good robustness under real traffic scenarios.

This paper presents a novel approach for estimating the vehicle's trajectory in complex urban environments. In previous work, we presented a visual odometry solution that estimates frame-to-frame motion from a single camera based on Random Finite Set (RFS) Statistics. This paper extends that work by combining the stereo cameras and gyroscope sensor. We are among the first to apply RFS statistics to visual odometry in real traffic scenes. The method is based on two phases: a preprocessing phase to extract features from the image and transform the coordinates from the image space to vehicle coordinates; a tracking phase to estimate the egomotion vector of the camera. We consider features as a group target and use the Probability Hypothesis Density (PHD) filter to update the overall group state as the motion vector. Compared to other approaches, our method presents a recursive filtering algorithm that provides dynamic estimation of multiple-targets states in the presence of clutter and high association uncertainty. The experimental results show that this method exhibits good robustness under various scenarios.