Abstract

The 2WIDE_SENSE (WIDE spectral band & WIDE dynamics multifunctional imaging SENSor Enabling safer car transportation) EU funded project is aimed at the development of a low-cost camera sensor for Advanced Driver Assistance Systems (ADAS) applications able to acquire the full visible to Short Wave InfraRed (SWIR) spectrum from 400 to 1700 nm. This paper presents the first results obtained by investigating the SWIR contribution to pedestrian detection in difficult visibility conditions as haze and fog employing the wide-bandwidth camera developed within the project. Document type: Part of book or chapter of book

Abstract

The paper considers semi-global stereo matching in the context of vision-based driver assistance systems. The need for real-time performance in this field requires a design change of the originally proposed method to run on current hardware. This paper proposes such a new design; the novel strategy first generates a disparity map from half-resolution input images. The result is then used as prior to restrict the disparity search space for full-resolution computation. This approach is compared to an SGM strategy as employed currently in a state-of-the-art real-time FPGA solution. Furthermore, trinocular stereo evaluation is performed on ten real-world traffic sequences with a total of 4,000 trinocular frames. An extension to the original evaluation methodology is proposed to resolve ambiguities and to incorporate disparity density in a statistically meaningful way. Evaluation results indicate that the novel SGM method is up to 40% faster when compared to the previous strategy. It returns denser disparity maps, and is also more accurate on evaluated traffic scenes. Document type: Part of book or chapter of book

Abstract

When a vehicle equipped with an artificial vision system enters or exits a tunnel, the camera may temporarly suffer from reduced visibility, or even get completely blind due to quick changes in enviromental illumination.\r \r This paper presents a vision-based system that detects approaching tunnels entrances or exits. The proposed system allows other ADAS (Advanced Driver Assistance Systems) to act on camera parameters to effectively avoid the tunnel blindness effect. Information regarding approaching tunnel entrance can be helpful for other sensors as well and for sensor fusion systems. In terms of path planning, this system can also inform GNSS-based systems (Global Navigation Satellite System), which usually do not receive any signal in tunnels, and trigger dead reckoning techniques.\r \r The proposed system is noticeably fast and therefore well fit to be used as a background process to support other ADAS applications. Document type: Part of book or chapter of book

Abstract

In the recent years, more and more modern cars have been equipped with perception capabilities. One of the key applications of such perception systems is the estimation of a risk of collision. This is necessary for both Advanced Driver Assistance Systems and Autonomous Navigation. Most approach for risk estimation propose to detect and track the dynamic objects in the scene. Then the risk is estimated as a Time To Collision (TTC) by projecting the object’s trajectory in the future. In this paper, we propose a new grid-based approach for collision risk prediction, based on the Hybrid-Sampling Bayesian Occupancy Filter framework. The idea is to compute an estimation of the TTC for each cell of the grid, instead of reasoning on objects. This strategy avoids to solve the difficult problem of multi-objects detection and tracking and provides a probabilistic estimation of the risk associated to each TTC value. After promising initial results, we propose in this paper to evaluate the relevance of the method for real on-road applications, by using a real-time implementation of our method in an experimental vehicle. Document type: Part of book or chapter of book

Abstract

In recent years, driver assistance systems have become a strong trend in automotive engineering. Such systems increase safety and comfort by supporting the driver in critical or stressful traffic situations. A great variety of surround sensors with different fields of view include radar, ultrasonic, laser, and vision systems. These sensors are based on different technologies and measurement principles. They all have their specific advantages and disadvantages and range from low-cost to high-end systems. They also differ in size, mounting position, maintenance, and weather compatibility. Hence, such sensors are used in various configurations to explore the surroundings ahead, sideways, and behind a vehicle. In addition, vehicle dynamics information from speed, steering angle, yaw rate, and acceleration sensors is available. Data fusion algorithms on raw data, feature, or object levels are used to collect all this information and set up vehicle surround models. An important issue in this context is the question of data accuracy and reliability. Situation interpretation of the traffic scene is based on these surround models. Any situation interpretation has to be performed in real-time, independent of the situation complexity. Typically, the prediction horizon is a couple of seconds. Depending on the results of the driving environment analysis critical situations can be identified. In consequence, the driver can be informed or warned. Some driver assistance systems already perform driving tasks like following, lane changing, or parking autonomously. The art of designing new, valuable driver assistance systems includes many factors and aspects and is still an engineering challenge in automotive research. Document type: Part of book or chapter of book

Abstract

Proceeding of: 8th International Conference, ACIVS 2006, Antwerp, Belgium, September 18-21, 2006 This paper presents a Road Detection and Classification algorithm for Driver Assistance Systems (DAS), which tracks several road lanes and identifies the type of lane boundaries. The algorithm uses an edge filter to extract the longitudinal road markings to which a straight lane model is fitted. Next, the type of right and left lane boundaries (continuous, broken or merge line) is identified using a Fourier analysis. Adjacent lanes are searched when broken or merge lines are detected. Although the knowledge of the line type is essential for a robust DAS, it has been seldom considered in previous works. This knowledge helps to guide the search for other lanes, and it is the basis to identify the type of road (one-way, two-way or freeway), as well as to tell the difference between allowed and forbidden maneuvers, such as crossing a continuous line. Publicado Document type: Part of book or chapter of book

Abstract

Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane detection system based on a novel lane feature extraction approach. The robustness and real-time of algorithm enable different configurations of embedded solutions. The system is divided into three phases. Firstly, using the Prewitt operator we can get the rich useful details and using Shen Jun operator we can get step edge, on the other hand Shen Jun operator is the best filter to detect the symmetrical markings according to the maximum signal noise ratio (SNR) criterion. So we introduce the best compromise method between noise smoothing and edge locating that combining the Prewitt operator with Shen Jun operator to extract lane markings. Then a fast Hough transform based on image pyramid is applied to get the lane lines. The posterior algorithm of reasonably refining the Lane lines angle is introduced to correct to error caused by Hough transform. Finally, robust detection of vehicle’s departure warning is also discussed. Experiment results on real road will be presented to prove the robustness and effectiveness of the proposed lane detection algorithm. Document type: Part of book or chapter of book

Abstract

Current developments in Car2X communication technology provide the basis for novel driver assistance systems. To assess the impact of such a system on a group of cars approaching a non moving traffic jam, the driver behavior resulting from a system which supports anticipatory driving is modeled and used in a sub-microscopic traffic simulation. By equipping various percentages of a simulated group of cars with the model, the effect of the system on maximum deceleration and fuel consumption can be assessed. Finally the problems resulting from switching between different driver models are discussed. Document type: Part of book or chapter of book

Abstract

International audience; Embedded systems have developed from single microcontrollers to networked sys-tems and are moving further on to large open systems. As an example, automotive electronics started as a single microcontroller for engine control to develop into a local network of 50 and more electronic control units connected via several network standards and gateways which are found in current cars. These networks will be ex-tended by open wireless car-to-car or car-to-infrastructure communication enabling completely new functionality, such as advanced driver assistance systems that report approaching cars that could cause an accident. Other examples are found in health-care, where patients are monitored at home connected to a hospital data base and monitoring system rather than staying in the hospital for that purpose, or in smart buildings where different control functions are integrated to minimize energy con-sumption and adapt consumption to the available energy, or in energy supply net-works that are optimized to include renewable energy production. In all these cases we observe a transition from local closed networks with a single systems integrator controlling all design aspects (such as an automotive manufacturer) to larger open networks with many independent functions and different integrators following differ-ent design objectives. The Internet plays an important role supporting that trend. Unlike closed networks with a defined topology, such systems change over the life-time of a system. Document type: Part of book or chapter of book

Abstract

The increasing importance of outdoor applications such as driver assistance systems or video surveillance tasks has recently triggered the development of optical flow methods that aim at performing robustly under uncontrolled illumination. Most of these methods are based on patch-based features such as the normalized cross correlation, the census transform or the rank transform. They achieve their robustness by locally discarding both absolute brightness and contrast. In this paper, we follow an alternative strategy: Instead of discarding potentially important image information, we propose a novel variational model that jointly estimates both illumination changes and optical flow. The key idea is to parametrize the illumination changes in terms of basis functions that are learned from training data. While such basis functions allow for a meaningful representation of illumination effects, they also help to distinguish real illumination changes from motion-induced brightness variations if supplemented by additional smoothness constraints. Experiments on the KITTI benchmark show the clear benefits of our approach. They do not only demonstrate that it is possible to obtain meaningful basis functions, they also show state-of-the-art results for robust optical flow estimation. Document type: Part of book or chapter of book