International Journal of Mecanical Engineering and Tecnology (IJMET) Volume 9, Issue 5, May 2018, pp. 1058 1063, Article ID: IJMET_09_05_116 Available online at ttp://www.iaeme.com/ijmet/issues.asp?jtype=ijmet&vtype=9&itype=5 ISSN Print: 0976-6340 and ISSN Online: 0976-6359 IAEME Publication Scopus Indexed ON THE REINFORCED RELIABILITY OF FORWARD COLLISION WARNING SYSTEM WITH MACHINE LEARNING Jongwon Kim and Jeongo Co Department of Electrical Engineering, Sooncunyang University, Asan, Korea ABSTRACT Researces on Advanced Driving Assistance System (ADAS) ave been increased and ADAS as become a major field in automotive industry over te past few decades. One of te essential means comprising ADAS is Forward Collision Warning System (FCWS), wic, from te safety point of view, sould be designed to ave ig reliability and robustness against disturbance in noisy environment. FCWS in te market, owever, still ave vulnerability sensitive to te driving conditions and mounted sensors. We tus introduce an integrity monitoring approac on FCWS using te veicle braking distance predictor wic employs te neural network to predict te braking distance and aims to improve te reliability of te FCWS by preventing te accident by judging te abnormality of te system. Te performance of te proposed veicle braking distance predictor demonstrated te superiority in term of te prediction capability and robustness against te external noise compared wit te existing matematical braking distance model. Keywords: advanced driving assistance system, forward collision warning system, macine learning, braking distance model Cite tis Article: Jongwon Kim and Jeongo Co, On te Reinforced Reliability of Forward Collision Warning System wit Macine Learning, International Journal of Mecanical Engineering and Tecnology, 9(5), 2018, pp. 1058 1063 ttp://www.iaeme.com/ijmet/issues.asp?jtype=ijmet&vtype=9&itype=5 1. INTRODUCTION Te 4 t Industrial Revolution refers to te industrial canges tat are expected to build virtual pysical systems integrating real and virtual robot or artificial intelligence to automatically and intelligently control objects and it as been received wide attention over te past few years. Te fields of te future industry of te 4 t Industrial Revolution include autonomous driving tecnology tat can recognize and judge te road situation by temselves witout ant intervention by driver, Internet of Tings (IoT), Artificial Intelligent (AI), Virtual Reality (VR), and so on.[7-8] Among tese, autonomous driving is currently in te flow of rapid development, and one of te key features mounted on autonomous veicles is te Advanced ttp://www.iaeme.com/ijmet/index.asp 1058 editor@iaeme.com
On te Reinforced Reliability of Forward Collision Warning System wit Macine Learning Driver Assistance System (ADAS) wic provides steering control to track lanes on te road, warning of collisions, speed control for smoot traffic flow and prevention of collision, etc.[3] In particular, te Forward Collision Warning System (FCWS) illustrated in Figure 1 is te most important and tus widely researced and developed field in recent years. Te data used in te calculation of te FCWS are mega data and micro data, and te mega data is sared information, suc as traffic volume and average speed, and te micro data are information suc as te speed and te distance between cars collected by te individual veicle sensor.[1] Figure 1 Illustration of FCWS To obtain te micro data, it is possible to use various sensors suc as radar, laser scanner, and video camera, but eac sensor as its limitations.[2] Te radar is excellent for accurate veicle detection and over 100m long rage detection, but te accuracy of stopping and proximity detection is somewat lower. In te case of a laser scanner, proximity veicles and obstacles are outstanding, but te detection distance is sort and making it difficult to detect long-distance veicles and expensive. Te detection performance of RGB camera as certain limitations due to te dependence of illumination and weater, and it is not able to calculate te exact distance to te object. Previous researc aimed at designing braking distance models using different kinds of algoritms suc as fractional energy rates and tire models.[5] However, based on standardized data, real-time FCWS may not be capable to be applied due to unexpected noise and individual veicle conditions. In tis paper, we tus propose a veicle braking distance prediction system for accident prevention and reinforcement by more stable detection of te limitation point and te possibility of forward collision inerent in te distance measurement function of FCWS wic sould be guaranteed in ig stability and reliability. For braking distance prediction system implementation, Neural Networks (NN) can be a good candidate for consideration. NN is a family of macine learning algoritms known for its powerful features suc as problem solving, optimization, and pattern classification of nonlinear data.[1] Te existing FCWS is reinforced by te proposed Veicle Braking Distance Predictor based on Neural Networks (VBDP-NN) wic assists to determine te integrity of te FCWS by comparing of te predicted braking distance from VBDP-NN and te estimated braking distance from te existing matematical Veicle Braking Distance Model (VBDM). Troug tis, we aimed to prevent a forward collision accident caused by sensor malfunction and traveling noise and to improve te reliability of FCWS. After optimizing te proposed prediction system troug strategic learning, we evaluated te predictive power and robustness against noise by comparing wit te conventional VBDM, te proposed prediction system sowed tat te braking distance prediction error is greatly reduced and te performance is outstanding in te ig-speed region were te robustness against te external environmental noise becomes important. ttp://www.iaeme.com/ijmet/index.asp 1059 editor@iaeme.com
Jongwon Kim and Jeongo Co 2. PRELIMINARIES ON NEURAL NETWORKS Neural Networks (NN) tat matematically model uman recognition processes and neural states ave excellent performance in pattern analysis, optimization, and approximation.[6] Multi-Layer Perceptron (MLP) among many neural network structures is a great nonlinear problem-solving metod and is applied to various fields suc as signal processing, control, speec recognition, etc.[2] te structure of te NN wit several idden nodes in a single idden layer and a single output node is represented in Figure 2, and te connection weigt w is adjusted troug te activation function by syntesizing te weigt wit respect to te input value 1,2, L. Ten te output values of te idden layer and te output layer are represented by (1). =, = 1 were is te weigt connected between layers and j and is te weigt between layers and z. 1,2, H is te output from te previous input nodes, and z is an output from te previous idden nodes. is te sigmoid function formed as! = 1 1 # $%, an activation function, generally used to transform te linear saturation function into a differentiable form. Te learning data of te neural networks are varied in size and range, so tat normalization may be preceded for stable and fast learning in general. Neural Networks sould find an optimal parameter,, tat adjusts to minimize te error between te output value and te target value troug learning.[1] Te error function E is minimized based on a back-propagation (BP) learning algoritm tat can efficiently obtain te slope of te parameter in a least square sense. Based on te error function, te weigt of te neural networks is ten updated as (2). ( +1 = (+* +, +-./ ( +1 = (+* +, +- 0. 2 Figure 2 Internal structure of neural networks 3. REINFORCED FORWARD COLLISION WARNING SYSTEM In general, te stopping distance of te veicle consists of te braking distance and te reaction distance. Te braking distance is defined as te distance traveled from te start of braking to te moment of stopping and te distance moved during te reaction time is called te reaction distance [9]; te time taken for te driver to start braking after stopping on te brakes after recognizing te danger is called te reaction time as illustrated in Figure 3. ttp://www.iaeme.com/ijmet/index.asp 1060 editor@iaeme.com
On te Reinforced Reliability of Forward Collision Warning System wit Macine Learning Figure 3 Illustration of stopping distance Te braking distance is primarily affected by te speed of te veicle and te coefficient of friction between te tires and te road surface, and is expressed as (3). 1 2 = 3 4 56 8.: 4 ; < 3 were = is an acceleration of gravity, 9.81 @ B A 5, C is te speed of veicle and D E is te coefficient of friction of te veicle depending on te road surface condition. Coefficient of friction is maximum at dry and low speed due to te influence of speed and road surface, and minimum at wet and ig speed.[4] In order to reinforce te existing FCWS, te Veicle Braking Distance Predictor based on Neural Networks (VBDP-NN) is proposed as depicted in Figure 4 and is trained by te driving data set assuming tat te reaction distance is immediate and te speed is limited to te range of 50~100 I@ B wic is te standard of te motorway two-lane or more road. Also, taking into account te influence of various micro-driving environments tat affect te friction coefficient, random values witin te flat aspalt maximum speed criterion are added to te coefficient of friction to te slope value representing te slope of te road. Te inputs to VBDP-NN are te speed of veicle and te coefficient of friction of te veicle, and te output of VBDP-NN is te braking distance. Figure 4 Block diagram of reinforced FCWS constructed by VBDP-NN 4. SIMULATION RESULTS To evaluate te proposed VBDP-NN, te arcitecture of VBDP-NN was constructed troug strategic learning. Te learning rate was selected as 0.45 for dry road and 0.55 for wet road surface considering stability and reduction rate according to eac road surface condition. Te number of idden nodes was cosen as 2 since te error did not decrease any longer regardless of te road surface condition at 2. Since te actual driving environment as a possibility of noise due to various situations and variables, wic cannot be predicted, te VBDP-NN sould be evaluated not only for simple numerical prediction but also for stability and robustness in a noisy environment. In order to verify tis, test data including 5~25% random noise was generated and te stability and robustness of predicted values were evaluated troug comparison of prediction error distances wit VBDM, wic is a standard model. ttp://www.iaeme.com/ijmet/index.asp 1061 editor@iaeme.com
Jongwon Kim and Jeongo Co a b c Figure 5 Performance comparisons of VBDP-NN and VBDM wen (a) 5% noise (b) 15% noise (c) 25% noise Table 1 Performance evaluation of VBDP-NN in terms of mean squared prediction error VBDP-NN VBDM Speed L I@ B M Noise % 50-85 85-100 50-85 85-100 DRY Condition 5% 6.7 34 6.4 47 15% 56 185 57 458 25% 140 480 205 1622 WET Condition 5% 10 73 11 102 15% 88 403 89 1092 25% 375 1045 378 4338 Table 1 sows te results of te performance evaluation according to te influence of te speed zone, te road surface condition and te noise, and te prediction error distance, wen 5~25% of noise is included in te input data according to te road surface condition, is sown in figure 5 were te VBDP-NN sown similar predictive performance to VBDM wit an approximate prediction error difference of less tan 1% regardless of te road surface condition at a low speed of 85 I@ B or less. However, it sowed 25%~ more difference predictability compared to VBDM. In particular, te robustness to noise of te VBDP-NN was increased as te level of noise interference increases. In te noisy environment of 15~25% and te ig-speed section over 85 I@ B, te prediction error from VBDP-NN was about 2.5~4 times lower tan VBDM regardless of te road surface condition. 5. CONCLUDING REMARKS In tis paper, we proposed a reinforced forward collision warning system by predicting veicle braking distance to prevent possible accidents wen te forward collision warning system, wic is used in te active control stage of te autonomous veicle, is operated alone. Te proposed reinforcement system detects te error by judging weter te system is abnormal due to te malfunction of te sensor and te noise by comparing te braking distances obtained from te existing forward collision warning system as well as te predicted value of te proposed VBDP-NN. In order to overcome te inaccuracy problem of te prediction from individual driving environment data, wic is based on te standardized model, te proposed system adopts neural networks, wic is a field of macine learning. After te optimal design of te neural networks, we compared wit te existing veicle braking distance model. As a result of comparing te performance wit te existing braking distance model, te proposed VBDP-NN sowed superior in te ig-speed region were te stability and braking distance for noise tat may occur in te actual driving environment are increased rapidly. Te proposed veicle braking distance prediction system as proved ig ttp://www.iaeme.com/ijmet/index.asp 1062 editor@iaeme.com
On te Reinforced Reliability of Forward Collision Warning System wit Macine Learning predictive power and noise robustness troug performance tests and can be expected to be used as a reinforcement system of actual FCWS. 6. ACKNOWLEDGEMENT Tis work was supported by Academic Activity Support Project from Sooncunyang University. REFERENCES [1] Cao, W., Wang, X., M., Ming, Z. et al. A review on neural networks wit random weigts. Neurocomputing Vol. 275 (2018) pages. 278-287 [2] Castañeda-Miranda, A., and Castaño, V.M. Smart frost control in greenouses by neural networks models. Computers and Electronics in Agriculture Vol. 137 (2017) pages. 102-114 [3] Caaban, K., Sawky, M., and Crubile, P. A distributed embedded arcitecture for te evaluation of ADAS systems. In: proceeding of te 12t IFAC Symposium on Control in Transportation Systems. Redondo Beac, CA, USA, pages. 237-244 [4] Fricke, L.B. Traffic Accident Reconstruction: Volume 2 of te Traffic Accident Investigation Manual. Te traffic Institute, Evanston, 1990. [5] Jeon, D.Y., Coi, J.H., Co, J.R. et al. Braking Distance Estimation using Frictional Energy Rate. In: Proceedings of te KSME 2004 spring annual meeting. Yongin, Korea, pages. 519-52 [6] Nawi, N.M., Kan, A., and Reman, M.Z. A new Levenberg-Marquardt Based Back Propagation Algoritm Trained wit Cuckoo Searc. In: proceeding of te 4t International Conference on Electrical Engineering and Informatics. (ICEEI 2013). Procedia Tecnology, Malaysia, 2013, pages. 18-23 [7] Ooi, K.B. Cloud computing in manufacturing: Te next industrial revolution in Malaysia. Expert Systems wit Applications Vol. 93 (2018) pages. 376-394 [8] Tak, S.H., Woo, S.M., and Yeo, H.S. Study on te framework of ybrid collision warning system using loop detector and veicle information. Transportation Researc Part C: Emerging Tecnologies. Vol. 73 (2016) pages. 202-218 [9] Taoka, G.T. Brake Reaction Times of Unalerted Drivers. Institute of Transportation Engineers Vol. 59 (1989) pages. 19-21 ttp://www.iaeme.com/ijmet/index.asp 1063 editor@iaeme.com