In recent years, innovative passive safety concepts have been developed that have the potential to further decrease the number of victims of traffic accidents. Such complex passive safety systems (i.e. the Daimler PRE-SAFE Pulse or inflating metal structures in the vehicle doors) typically require lead times for activation/preparation that are longer than classical crash-detecting acceleration or pressure sensors can offer. These concepts require a close integration with active safety systems in order to allow an early assessment of the type, direction, and severity of an imminent collision. This contribution proposes a prototypical side collision detection system that is based on a monocular camera positioned in the side mirrors. For collision detection, the system fuses the detection results from optical flow and a warped bird's eye view in order to allow a robust system reaction in case of an imminent collision with a dynamic object.

The vehicle-following concept has been widely used in several intelligent-vehicle applications. Adaptive cruise control systems, platooning systems, and systems for stop-and-go traffic employ this concept: The ego vehicle follows a leader vehicle at a certain distance. The vehicle-following concept comes to its limitations when obstacles interfere with the path between the ego vehicle and the leader vehicle. We call such situations dynamic driving situations. This paper introduces a planning and decision component to generalize vehicle following to situations with nonautomated interfering vehicles in mixed traffic. As a demonstrator, we employ a car that is able to navigate autonomously through regular traffic that is longitudinally and laterally guided by actuators controlled by a computer. This paper focuses on and limits itself to lateral control for collision avoidance. Previously, this autonomous-driving capability was purely based on the vehicle-following concept using vision. The path of the leader vehicle was tracked. To extend this capability to dynamic driving situations, a dynamic path-planning component is introduced. Several driving situations are identified that necessitate responses to more than the leader vehicle. We borrow an idea from robotics to solve the problem. Treat the path of the leader vehicle as an elastic band that is subjected to repelling forces of obstacles in the surroundings. This elastic-band framework offers the necessary features to cover dynamic driving situations. Simulation results show the power of this approach. Real-world results obtained with our demonstrator validate the simulation results

Our main objective in this paper is to perform a cartography of a road scene into a reference frame at rest, where 3D measurements delivered by on-board sensors serve as input. The main sensors of our autonomous vehicle are two CCD cameras. Their pictures are combined using stereopsis to generate 3D data. We need dead reckoning to properly associate 3D data among the frames. This necessitates us to obtain a precise ego-motion estimation. Dead reckoning using only standard vehicle odometry can cause nonnegligible errors. We use stationary points in the scene to support the determination of our ego-motion. Two types of stationary objects are used: vertical landmarks such as traffic signs and lane markings are used to compensate positioning errors. Preliminary results show that cartography as proposed in this paper is beneficial to detect stationary objects but needs further work for fast moving objects.

Our main objective in the paper is to perform a cartography of a road scene into a reference frame at rest, where 3D measurements delivered by on-board sensors serve as input. The main sensors of our autonomous vehicle are two CCD cameras. Their pictures are combined using stereopsis to generate 3D data. We need dead reckoning to properly associate 3D data among the frames. This necessitates us to obtain a precise ego-motion estimation. Dead reckoning using only standard vehicle odometry (velocity and steering angle) can cause non-negligible errors, especially in situations where side slip or skidding occurs. We use stationary points in the scene to support the determination of our ego-motion. Two types of stationary objects are used: Firstly, stationary vertical landmarks such as traffic signs are used to compensate errors in our localization prediction. Secondly, lane markings measured in consecutive frames are used to compensate orientation errors. Preliminary results show that dead reckoning using stationary objects can vastly improve self-localization.

A crucial task for steering an autonomous vehicle along a safe path in a car following scenario is the lateral control. The sensory input of such a lateral control are the position coordinates of the leader vehicle. The following problem occurs: due to the distance between the leader vehicle and the autonomous ego-vehicle, the lateral control has to interpolate a trajectory between the two vehicles. Using as a trajectory either a straight line or a curve of constant curvature causes the ego-vehicle to deviate from the leader vehicle's trajectory. Given a system delivering 3D points of the leader vehicle with time lags, one has a handle to reconstruct the leader vehicle's trajectory. In addition, one has to compensate the motion of the ego-vehicle by using its motion parameters. Once this transformation is performed, the position coordinates of the leader vehicle are available in a coordinate system at rest. Knowing the position of the ego-vehicle in that coordinate system, one can select the trajectory point of the leader vehicle that is closest to the ego-vehicle as input to the lateral controller. This simple approach increases significantly the precision of car following systems. The algorithm is applied successfully to an autonomous car for platooning at small velocities.

Presents an approach to solve the localization problem, in which an observer is given a topographic map of an area and dropped off at an unknown location. The solution to this problem requires establishing correspondences between viewer-centered observable features and their location on the map. The feature the authors select is the panoramic horizon curve, defined as the sky-ground boundary perceived by the observer as he performs a full 360/spl deg/ in place. In the authors' approach, they first precompute, offline, these horizon curves at a set of locations on a grid, from the topological map. These curves are approximated by polygons with different line fitting tolerances to gain robustness to noise in the authors' representation. These polygons are grouped into overlapping super segments, which are then encoded and stored in a table. The online computation consists of acquiring the panoramic view and extracting (with human help) the horizon curve. This curve is approximated by a polygon and the resulting super segments, used as indices in the data base, allow one to retrieve candidate locations. The best candidate is selected during a verification step which applies geometric constraints. This process uses local features and can therefore tolerate significant occlusion likely to occur in real environments. The authors illustrate the performance of the approach on results obtained from real data.

An approach is presented to solve the localization problem, in which an observer is given a topographic map of an area and dropped off at an unknown location. The solution to this problem requires establishing correspondences between viewer-centered observable features and their location on the map. The feature selected is the panoramic horizon curve, defined as the sky-ground boundary perceived by the observer as a full 360 degrees rotation is performed. The authors propose to extract from many locations in the map the panoramic horizon curves which would be observed by the observer at each location. Such curves are encoded and stored in a table. To locate an unknown location, the panoramic horizon curve of the unknown location is first extracted, and then approximated by a family of polygons with different line fitting tolerances. By indexing into the table, candidate locations are retrieved. The correct candidate is found by applying further geometrical constraints in the verification step. The claims are validated by showing some results from a real map.>

The authors present an approach for the recognition of multiple 3-D object models from three 3-D scene data. The approach uses two different types of primitives for matching: small surface patches, where differential properties can be reliably computed, and lines corresponding to depth or orientation discontinuities. These are represented by splashes and 3-D curves, respectively. It is shown how both of these primitives can be encoded by a set of super segments, consisting of connected linear segments. These super segments are entered into a table and provide the essential mechanism for fast retrieval and matching. The issues of robustness and stability of the features are addressed in detail. The acquisition of the 3-D models is performed automatically by computing splashes in highly structured areas of the objects and by using boundary and surface edges for the generation of 3-D curves. The authors present results with the current system (3-D object recognition based on super segments) and discuss further extensions.>

The problem of recognition of multiple flat objects in a cluttered environment from an arbitrary viewpoint is addressed. The models are acquired automatically and approximated by polygons with multiple line tolerances for robustness. Groups of consecutive segments (super segments) are then encoded and entered into a table. This provides the essential mechanism for indexing and fast retrieval. Once the database of all models is built, the recognition proceeds by segmenting the scene into a polygonal approximation; the code for each super segment retrieves model hypotheses from the table. Hypotheses are clustered if they are mutually consistent and represent the instance of a model. Finally, the estimate of the transformation is refined. This methodology makes it possible to recognize models despite noise, occlusion, scale rotation translation, and a restricted range of weak perspective. A complexity bound is obtained.>

An approach for the recognition of multiple three-dimensional object models from three-dimensional scene data is presented. The authors work on dense data, but neither the models nor the scene data have to be complete. The problem is addressed in a realistic environment: the viewpoint is arbitrary, the objects vary widely in complexity, and no assumptions about the structure of the surface are made. The approach is novel in that it uses two different types of primitives for matching: small surface patches, where differential properties can be reliably computed, and lines corresponding to depth or orientation discontinuities. These are represented by splashes and 3-D curves respectively. It is shown how both of these primitives can be encoded by a set of super segments, consisting of connected linear segments. These super segments are entered into a hash table, and provide the essential mechanism for fast retrieval and matching.>

The problem of recognition of multiple flat objects in a cluttered environment from an arbitrary viewpoint (weak perspective) is addressed. The models are acquired automatically and initially approximated by polygons with multiple line tolerances for robustness. Groups of consecutive segments (supersegments) are then gray-coded and entered into a hash table. This provides the essential mechanism for indexing and fast retrieval. Once the database of all models is built, the recognition proceeds by segmenting the scene into a polygonal approximation; the gray code for each supersegment retrieves model hypotheses from the hash table. Hypotheses are clustered if they are mutually consistent and represent the instance of a model. The estimate of the transformation is refined. This methodology makes it possible to recognize models in the presence of noise, occlusion, scale, rotation, translation, and weak perspective. Unlike most of the current systems, its complexity grows as O(kN), where N is the number of models and k>