In this paper a novel spline-based multi-lane detection and tracking system is proposed. Reliable lane detection and tracking is an important component of lane departure warning systems, lane keeping support systems or lane change assistance systems. The major novelty of the proposed approach is the usage of the so-called Catmull-Rom spline in combination with the extended Kalman filter tracking. The new spline-based model enables an accurate and flexible modeling of the lane markings. At the same time the application of the extended Kalman filter contributes significantly to the system robustness and stability. There is no assumption about the parallelism or the shapes of the lane markings in our method. The number of lane markings is also not restrained, instead each lane marking is separately modeled and tracked. The system runs on a standard PC in real time (i.e. 30 fps) with WVGA image resolution (752 × 480). The test vehicle has been driven on the roads with challenging scenarios, like worn out lane markings, construction sites, narrow corners, exits and entries of the highways, etc., and good performance has been demonstrated. The quantitative evaluation has been performed using manually annotated video sequences.

This paper presents a novel approach to obtain reliable and robust time-to-contact estimates from a monocular moving camera observing various obstacles. The algorithm utilizes interest points to measure the relative scale change of an obstacle and applies robust estimation techniques to combine the different measurements into one of three possible motion models. These include a model with constant distance, with constant velocity and with constant acceleration. An interacting multiple model framework is used to select the appropriate model and finally to estimate the time-to-contact with the observed obstacle. The algorithm presented is evaluated utilizing a large set of recorded video sequences with radar ground truth. Due to its field of application the entire algorithm is designed to use as little computation time as possible and is thus realtime capable.

This paper presents an automotive video and radar data fusion framework that can be used as a preliminary stage of an automatic cruise control or collision mitigation by braking system. The fusion framework finds the optimal assignment of radar and camera target reports and provides improved state estimates for the fused targets. A sophisticated critical path selection is presented and used in the critical target selection module that aims to select the most relevant target. This module is capable of identifying targets that cut into the ego lane or cut out from the ego lane and incorporate that into the final target selection. The selected target is then compared to a state of the art algorithm within the radar sensor. Additional test drives were made to evaluate the performance of the new algorithm. Due to its low computational effort and the sensor independent design the presented algorithm is suitable to be used in the automotive embedded environment.

Nowadays, the camera online calibration module has been a fundamental and often requested component in an Advanced Driver Assistance System (ADAS). The proposed system provides an efficient and practical solution to such request, and also shows technical advances compared to earlier systems. It utilizes the lane markings for camera orientation calibration. At each image frame, multiple vanishing points of the lane markings are estimated using the weighted least squares method, followed by a tracking process with Kalman Filter for better consistency and robustness. With the filtered vanishing point the camera extrinsic tilt and pan angles can be estimated. Even though the proposed system relies on the lane markings for calibration, unlike other similar systems, the number of the lane markings in the system is not restricted, and the shape of the lane markings is also not restricted to straight lines or any other pre-defined models. A Monte Carlo evaluation scheme is devised for that purpose using real world driving sequences. The final result has shown that given an initial calibration error, in ±4 degree interval w.r.t. ground truth for both tilt and pan angles, the proposed system is capable of converging to accurate angles and providing consistent results.