Jan Effertz

Toward automated driving in cities using close-to-market sensors: An overview of the V-Charge Project

Future requirements for drastic reduction of CO2 production and energy consumption will lead to significant changes in the way we see mobility in the years to come. However, the automotive industry has identified significant barriers to the adoption of electric vehicles, including reduced driving range and greatly increased refueling times. Automated cars have the potential to reduce the environmental impact of driving, and increase the safety of motor vehicle travel. The current state-of-the-art in vehicle automation requires a suite of expensive sensors. While the cost of these sensors is decreasing, integrating them into electric cars will increase the price and represent another barrier to adoption. The V-Charge Project, funded by the European Commission, seeks to address these problems simultaneously by developing an electric automated car, outfitted with close-to-market sensors, which is able to automate valet parking and recharging for integration into a future transportation system. The final goal is the demonstration of a fully operational system including automated navigation and parking. This paper presents an overview of the V-Charge system, from the platform setup to the mapping, perception, and planning sub-systems.

Occupancy grid computation from dense stereo and sparse structure and motion points for automotive applications

We present a complete processing chain for computing 2D occupancy grids from image sequences. A multi layer grid is introduced which serves several purposes. First the 3D points reconstructed from the images are distributed onto the underlying grid. Thereafter a virtual measurement is computed for each cell thus reducing computational complexity and rejecting potential outliers. Subsequently a height profile is updated from which the current measurement is partitioned into ground and obstacle pixels. Different height profile update strategies are tested and compared yielding a stable height profile estimation. Lastly the occupancy layer of the grid is updated. To asses the algorithm we evaluate it quantitatively by comparing the output of it to ground truth data illustrating its accuracy. We show the applicability of the algorithm by using both, dense stereo reconstructed and sparse structure and motion points. The algorithm was implemented and run online on one of our test vehicles in real time.

Efficient Road Scene Understanding for Intelligent Vehicles Using Compositional Hierarchical Models

In this paper, we present a novel compositional hierarchical framework for road scene understanding that allows for reliable estimation of scene topologies, such as the number, location, and width of lanes and the lane topology, i.e., parallel, splitting, or merging. In our approach, lanes and roads are represented in a hierarchical compositional model in which nodes represent parts of roads and edges represent probabilistic constraints between pairs of parts. A key benefit of our approach is the representation of lanes and roads as a set of common parts. This makes our approach applicable to scenes with rich topological diversity, while bringing along the much desired computational efficiency. To cope with the high-dimensional and continuous parameter space of our model and the non-Gaussian image evidence, we perform inference using nonparametric belief propagation. Based on this approximate inference algorithm, we introduce depth-first message passing for lane detection, which performs inference in several sweeps. Empirical results show that depth-first message passing requires significantly lower computation for performance comparable with classical belief propagation.

Efficient scene understanding for intelligent vehicles using a part-based road representation

In this paper we propose a novel part-based approach to scene understanding, that allows us to infer the properties of traffic scenes, such as location and geometry of lanes and roads. Lanes and roads are parts of our undirected graphical model in which nodes represent parts or sub-parts of scenes and edges represent spatial constraints. Spatial constraints are statistically formulated and allow us to take advantage of low-level relations as well as high-level contextual information. The estimation of scene properties is formulated as an inference problem, which is solved using non-parametric belief propagation. Inferring about high-level scene properties, while relying on error-prone sensory cues is challenging and computational expensive. Therefore, we introduced a novel depth-first message passing scheme. This scheme is applied to several challenging real world scenarios showing robust results and real-time performance.

Object tracking from medium level stereo camera data providing detailed shape estimation using local grid maps

This paper presents a novel approach to environment perception providing consistent information about stationary obstacles, from an occupancy grid, and dynamic objects, from a model based object tracking at the same level of detail. Raw ranging data from dynamic objects is disregarded for the stationary grid map. A detailed shape representation of dynamic objects is achieved by using dedicated local grid maps for each track. In addition to that, a technique for evaluating tracking algorithms via assigned local object grid maps is presented and discussed. The algorithm is tested on different data sets and the obtained results are presented and discussed.

Improved object tracking from detailed shape estimation using object local grid maps with stereo

This paper presents a novel approach to environment perception providing detailed information for dynamic objects using occupancy grid maps. The shape representation of dynamic objects is derived from dedicated local grid maps. This allows for a precise contour estimation over time in terms of polylines. In addition to that, the local grid map is used to improve the object tracking by formulating measurements for the Kalman filter update overcoming partial occlusion and over-segmentation of raw data. The algorithm is tested on different data sets and initial results are presented and discussed.