IDF-Autoware: Integrated Development Framework for ROS-Based Self-Driving Systems Using MATLAB/Simulink

This paper proposes an integrated development framework that enables co-simulation and operation of a Robot Operating System (ROS)-based self-driving system using MATLAB/Simulink (IDF-Autoware). The management of self-driving systems is becoming more complex as the development of self-driving technology progresses. One approach to the development of self-driving systems is the use of ROS; however, the system used in the automotive industry is typically designed using MATLAB/Simulink, which can simulate and evaluate the models used for self-driving. These models are incompatible with ROS-based systems. To allow the two to be used in tandem, it is necessary to rewrite the C++ code and incorporate them into the ROS-based system, which makes development inefficient. Therefore, the proposed framework allows models created using MATLAB/Simulink to be used in a ROS-based self-driving system, thereby improving development efficiency. Furthermore, our evaluations of the proposed framework demonstrated its practical potential

Analysis of A Learning Algorithm with Distortion Free Constraint

金沢大学理工研究域　電子情報学系In Blind Source Separation (BSS), a eparation block is trained so as to make the output signals to be statistically independent. In this case, the independency is able to be increased by changing frequency response of the output signals, resulting in signal distortion. Especially, a feed-forward BSS (FF-BSS) has some degree of freedom in the separation block, and the signal distortion will be caused. The signal distortion is evaluated as difference between the output signal and the signal source in the measured signal. Some equations are derived from the conditions of complete separation and signal distortion free. They are used as the distortion free constraint in the conventional learning process [11]. On the other hand, a feedback BSS (FB-BSS) has a solution, which can satisfy both complete separation and distortion free. In this paper, the learning algorithm with the distortion free constraint is applied to the FF-BSS in time domain. Many kinds of signal sources are used in simulation in order to compare the proposed method and the conventional, in which difference between the output signals and the measured signals is included in the cost function [4]. Furthermore, the FB-BSS is also evaluated.ブラインド信号源分離では(BSS) は分離回路がその出力信号が統計的に独立になるように学習される．この場合，出力信号の周波数特性が変化することにより，独立性が高まることもあるので，信号歪みが生じる可能性がある．特に，フィードフォワード形BSS(FF-BSS)は分離回路における自由度が高く，信号歪みを生じる可能性がある．信号歪みの基準を観測信号に含まれる信号源と考え，完全分離の条件と信号無歪の条件から導かれた制約条件を学習に加味する信号歪み抑制学習法を提案した[11]．信号源をsi，観測信号をxi，出力信号をyi とするとき，信号を分離するとともにyi をxi におけるsi 成分に近づけることができる．これに対し，観測信号と出力信号の差を評価関数に追加する従来法では，観測信号に含まれる複数の信号源の影響で信号源分離が充分ではない．一方，フィードバック形BSS(FBBSS)では，信号源分離と信号歪み抑制の条件を同時に満たす回が存在する．本稿では信号歪み抑制学習法を時間領域で学習するFF-BSS に適用し，種々の信号源を 使って従来方式[4] と比較することによりその特性を解 析する．同時に，FB-BSS の有効性も検証する

Analysis of signal separation and signal distortion in feedforward and feedback blind source separation based on source spectra

金沢大学理工研究域　電子情報学系Source separation and signal distortion in three kinds of BSSs with convolutive mixture are analyzed. They include a feedforward BSS, trained in the time domain and in the frequency domain, and a feedback BSS, trained in the time domain. First, an evaluation measure of signal distortion is discussed. Second, conditions for source separation and distortion free are derived. Based on these conditions, source separation and signal distortion are analyzed. The feedforward BSS has some degree of freedom, and the output spectrum can be changed. The feedforward BSS, trained in the frequency domain, has weighting effect, which can suppress signal distortion. This weighting is, however, effective only when the source spectra are similar to each other. Since, the feedforward BSS, trained in the time domain, does not have any constraints on signal distortion free, its output signals can be easily distorted. A new learning algorithm with a distortion free constraint is proposed. On the other hand, the feedback BSS can satisfy both source separation and distortion free conditions simultaneously. Simulation results support the theoretical analysis. © 2005 IEEE

A distortion free learning algorithm for feedforward BSS and its comparative study with feedback BSS

金沢大学理工研究域　電子情報学系Source separation and signal distortion are theoretically analyzed for the FF-BSS systems implemented in both the time and frequency domains and the FB-BSS system. The FF-BSS systems have some degree of freedom, and cause some signal distortion. The FB-BSS has a unique solution for complete separation and distortion free. Next, the condition for complete separation and distortion free is derived for the FF-BSS systems. This condition is applied to the learning algorithms. Computer simulations by using speech signals and stationary colored signals are carried out for the conventional methods and the new learning algorithms employing the proposed distortion free constraint. The proposed method can drastically suppress signal distortion, while maintaining high separation performance. The FB-BSS system also demonstrates good performances. The FF-BSS systems and the FB-BSS system are compared based on the transmission time difference in the mixing process. Location of the signal sources and the sensors are rather limited in the FB-BSS system. © 2006 IEEE

A distortion free learning algorithm for feedforward BSS and its comparative study with feedback BSS

金沢大学大学院自然科学研究科Source separation and signal distortion are theoretically analyzed for the FF-BSS systems implemented in both the time and frequency domains and the FB-BSS system. The FF-BSS systems have some degree of freedom, and cause some signal distortion. The FB-BSS has a unique solution for complete separation and distortion free. Next, the condition for complete separation and distortion free is derived for the FF-BSS systems. This condition is applied to the learning algorithms. Computer simulations by using speech signals and stationary colored signals are carried out for the conventional methods and the new learning algorithms employing the proposed distortion free constraint. The proposed method can drastically suppress signal distortion, while maintaining high separation performance. The FB-BSS system also demonstrates good performances. The FF-BSS systems and the FB-BSS system are compared based on the transmission time difference in the mixing process. Location of the signal sources and the sensors are rather limited in the FB-BSS system. © 2006 IEEE

A learning algorithm with distortion free constraint and comparative study for feedforward and feedback BSS

金沢大学理工研究域　電子情報学系Source separation and signal distortion are theoretically analyzed for the FF-BSS systems implemented in both the time and frequency domains and the FB-BSS system. The FF-BSS systems have some degree of freedom, and cause some signal distortion. The FB-BSS has a unique solution for complete separation and distortion free. Next, the condition for complete separation and distortion free is derived for the FF-BSS systems. This condition is applied to the learning algorithms. Computer simulations by using speech signals and stationary colored signals are carried out for the conventional methods and the new learning algorithms employing the proposed distortion free constraint. The proposed method can drastically suppress signal distortion, while maintaining high separation performance. The FB-BSS system also demonstrates good performances. The FF-BSS systems and the FB-BSS system are compared based on the transmission time difference in the mixing process. Location of the signal sources and the sensors are rather limited in the FB-BSS system. © 2006 IEEE

A distortion free learning algorithm for feed-forward BSS with convolutive mixture and multi-channel signal sources

金沢大学理工研究域　電子情報学系畳み込み混合過程におけるフィードフォワード(FF-)形ブラインドソースセパレーション(BSS)では自由度が存在するため信号歪みが生じる.我々は,信号源-センサーが2チャンネルの場合において,完全分離と信号無歪みの条件を制約条件として課す信号歪み抑制学習アルゴリズムを時間領域,周波数領域のFF-BSSに対して提案してきた.本稿では,信号歪み抑制の制約条件を多チャンネルに拡張し,かつ,計算の複雑さを軽減するために制約条件を近似する方式を提案する.音声を用いたコンピュータシミュレーションによってその近似制約方式と厳密制約方式がほぼ同等の分離性能と信号歪み抑制が得られることを確認した.また,3チャンネルにおいても,従来方式より特性が改善されることを確認した. Feed-forward Blind Source Separation (FF-BSS) systems have some degree of freedom in the solution space, and signal distortion is likely to occur in convolutive mixtures. Previously, a condition for complete separation and distortion free has been derived for 2-channel FF-BSS. This condition has been applied to the learning algorithms as a distortion free constraint in both the time and frequency domains. In this paper, the condition is further extended to multiple channel FF-BSSs. This condition requires the a high computational complexity to be applied to the learning process as a constraint. An approximate constraint is proposed in order to relax the high computational load. In comparison with the original constraint, computer simulations have demonstrated that the approximation can obtain similar performances with respect to source separation as well as signal distortion using speech signals. Furthermore, the performances can be improved compared to the conventionals for three channels