Algorithm and Architecture Design of Vehicle Tracking for Intelligent Vehicle Cruise Control System
Date Issued
2010
Date
2010
Author(s)
Tsai, Chih-Chung
Abstract
In this thesis, future vision-based intelligent cruise control system is targeted. A knowledge-based intelligent vehicle tracking system is proposed to satisfy the demands of safety and energy-saving purposes. In the trend of future intelligent vehicle, safety and energy efficiency are two critical developing goals. Intelligent cruise control, which satisfies both safety and energy efficiency purposes, is an important vehicle application for that. The developing trend also shows, with the enhancement of video analysis technologies, computer vision will play an important role in the future cruise system. Therefore, this thesis explores and researches on a vision-based vehicle cruise. A vehicle recognition and tracking system is discussed and developed by three aspects of performance, cost, and system specification.
First the impact of video specification on vehicle cruise control is discussed. By the energy consumption simulation for driving cycles, we can noticed that in automatic cruising condition, the detection accuracy of relative velocities of surrounding vehicles directly affects the energy efficiency. However, the velocity detection accuracy is influenced by the video specification. Considering the
vehicle safety and energy efficiency issues, we expect that the proposed system can support up to super high resolution (4096x2160), and 10-frame-per-second computing throughput can be reached under that resolution.
The system of vehicle recognition and tracking is explored as follows in this thesis. In the system, the module of vehicle recognition is responsible to recognize
vehicle positions in image and output them to the vehicle tracking modulefor tracking and range detection. Though learning-based recognition algorithms perform high recognition rate and reliability, there are limitations on the abilities of object positioning, object size determination and false alarm rate minimization. Facing to these error conditions, several common object tracking algorithms are discussed. It can be found that current related algorithms are incapable of solving the problem of error position initialization and cannot be practically applied in vision-based cruise system. Therefore, a knowledge-based intelligent vehicle tracking algorithm is proposed. The algorithm is developed with the existed
knowledge of vehicle characteristics, and it possesses two main functionalities, position auto-adjustment and false alarm reduction. As experimental results show, the proposed knowledge-based algorithm performs better ability of range
detection, and it also effectively reduces the system false alarm rate. Moreover, due to the resistance against the departure error of initial position, a recognitionand-
tracking parallel processing scheme can be applied instead of the sequential processing, which reduces a large amount of system memory cost.
One of the essential factors is the required system throughput under the targeted high image resolution. In the last part, the execution timing performance of proposed tracking algorithm is analyzed. A hardware-oriented algorithm optimization methodology and its corresponding hardware architecture are proposed for acceleration. During hardware design, different optimization techniques are
applies to the architecture. The system after hardware acceleration reaches the processing speed of 81.4 frames per second under 1280x960 image resolution, and it can support up to 4096x2160 image resolution with 11 frames per second processing speed, which fits the specification of cruise control system. The hardware is finally implemented with UMC 90nm Logic Low-K SP-RVT Process technology. The total chip size is 2.2x2.2mm2 with 12.8Kbits on-chip memory.
Operating frequency is 100MHz and the minimum and maximum powers are 23.45mW and 648.75mW, respectively. Maximum five targets can be tracked simultaneously.
Subjects
cruise control
vehicle tracking
algorithm
hardware architecture
SDGs
Type
thesis
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