Sensor fusion methodology for vehicle detection

A novel sensor fusion methodology is presented, which provides intelligent vehicles with augmented environment information and knowledge, enabled by vision-based system, laser sensor and global positioning system. The presented approach achieves safer roads by data fusion techniques, especially in single-lane carriage-ways where casualties are higher than in other road classes, and focuses on the interplay between vehicle drivers and intelligent vehicles. The system is based on the reliability of laser scanner for obstacle detection, the use of camera based identification techniques and advanced tracking and data association algorithms i.e. Unscented Kalman Filter and Joint Probabilistic Data Association. The achieved results foster the implementation of the sensor fusion methodology in forthcoming Intelligent Transportation Systems

Review of Environment Perception for Intelligent Vehicles

Overview of environment perception for intelligent vehicles supposes to the state-of-the-art algorithms and modeling methods are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. Integrated lane and vehicle tracking for driver assistance system that improves on the performance of both lane tracking and vehicle tracking modules. Without specific hardware and software optimizations, the fully implemented system runs at near-real-time speeds of 11 frames per second. On-road vision-based vehicle detection, tracking, and behavior understanding. Vision based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor–vision fusion for on-road vehicle detection. The traffic sign detection detailing detection systems for traffic sign recognition (TSR) for driver assistance. Inherently in traffic sign detection to the various stages: segmentation, feature extraction, and final sign detection

Development of nonlinear real-time intelligent controllers for anti-lock braking system (ABS)

The objective of the Anti-lock Braking System (ABS) is to control the wheel slip to maximize the friction coefficient between the wheel and the road, irrespective of the road surface and condition. The introduction of new braking system in road vehicles such as the electromechanical brakes used in brake-by-wire (BBW) system, which has a more continuous braking operation with a high level of accuracy, necessitates the continual review and improvement of the anti-lock braking system. From the control view point, therefore, more refinement of the ABS operation could be achieved with these improved hardware components. This thesis proposes a hybrid controller; combining feedback linearization and proportional, integral and derivative (PID) controllers, and a neural network-based feedback linearisation wheel slip controller. Furthermore, the thesis investigated the viability of a hybrid system of the proposed neural network and a (PID) wheel slip controller system. The hybrid systems, combines the accuracy of slip tracking ability of the PID controller and the robustness of the feedback linearization controller to achieve shorter stopping distance and good slip tracking. The performance of the proposed ABS systems are validated in software simulation and on a laboratory ABS test bench. The results for both controllers revealed their robustness to different road conditions and good slip tracking. This work further confirms the feasibility of a future neural network-based ABS controllers in road vehicles. Keywords: Anti-lock braking systems, Wheel slip, Friction models, Neural networks, PID controller, Feedback linearization controller, Intelligent controller, Hybrid controllers, Realtime embedded systems

Multi-Object Tracking with Interacting Vehicles and Road Map Information

In many applications, tracking of multiple objects is crucial for a perception of the current environment. Most of the present multi-object tracking algorithms assume that objects move independently regarding other dynamic objects as well as the static environment. Since in many traffic situations objects interact with each other and in addition there are restrictions due to drivable areas, the assumption of an independent object motion is not fulfilled. This paper proposes an approach adapting a multi-object tracking system to model interaction between vehicles, and the current road geometry. Therefore, the prediction step of a Labeled Multi-Bernoulli filter is extended to facilitate modeling interaction between objects using the Intelligent Driver Model. Furthermore, to consider road map information, an approximation of a highly precise road map is used. The results show that in scenarios where the assumption of a standard motion model is violated, the tracking system adapted with the proposed method achieves higher accuracy and robustness in its track estimations