We propose a trajectory planning approach for autonomous vehicles in highly dynamic traffic scenarios, using elastic bands to follow the observed trajectories of other vehicles. The focus of this paper is on the initialization of the elastic band. The proposed method does not rely on a map. We tested our method using recorded urban traffic data. The results show that the presented approach is valid and the proposed initialization process is clearly superior to naive initialization.

Environment sensing is an essential property for autonomous cars. With the help of sensors, nearby objects can be detected and localized. Furthermore, the creation of an accurate model of the surroundings is crucial for high-level planning. In this paper, we focus on vehicle detection based on stereo camera images. While stereoscopic computer vision is applied to localize objects in the environment, the objects are then identified by image classifiers. We implemented and evaluated several algorithms from image based pattern recognition in our autonomous car framework, using HOG-, LBP-, and Haar-like features. We will present experimental results using real traffic data with focus on classification accuracy and execution times.

In this paper we propose a trajectory planning approach for autonomous vehicles in highly dynamic traffic scenarios, capable of exploiting the observed trajectories of other vehicles. For this purpose, we introduce a novel variant of the timed elastic bands (TEB) approach by using fixed time intervals and a flexible goal position. We tested our method with a simulated merge-into-traffic scenario and compare it to a reference TEB implementation with focus on the impact of important parameters and the stability of planned trajectories. The results show that our method is an improvement over TEB in terms trajectory smoothness and stability.

This paper presents a system which constantly monitors the level of attention of a driver in traffic. The vehicle is instrumented and can identify the state of traffic-lights, as well as obstacles on the road. If the driver is inattentive and fails to recognize a threat, the assistance system produces a warning. Therefore, the system helps the driver to focus on crucial traffic situations. Our system consists of three components: computer vision detection of traffic-lights and other traffic participants, an eye tracking device used also for head localization, and finally, a human machine interface consisting of a head-up display and an acoustic module used to provide warnings to the driver. The orientation of the driver's head is detected using fiducial markers visible in video frames. We describe how the system was integrated using an autonomous car as experimental ADAS platform.

Semi-Global Matching (SGM) is widely used for real-time stereo vision in the automotive context. Despite its popularity, only implementations using reconfigurable hardware (FPGA) or graphics hardware (GPU) achieve high enough frame rates for intelligent vehicles. Existing real-time implementations for general purpose PCs use image and disparity sub-sampling at the expense of matching quality. We study methods to improve the efficiency of SGM on general purpose PCs, through fine grained parallelization and usage of multiple cores. The different approaches are evaluated on the KITTI benchmark, which provides real imagery with LIDAR ground truth. The system is able to compute disparity maps of VGA image pairs with a disparity range of 128 values at more than 16 Hz. The approach is scalable to the number of available cores and portable to embedded processors.