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Welcome to Journal of Automotive Safety and Energy,
汽车安全与节能学报

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  • 2022, Vol. 13 No. 2 Published on:30 June 2022 Previous issue    Next issue
    Review, Progress and Prospects
    Hydrogen fuel cell vehicle technology roadmap and progress in China
    WANG Hewu, OUYANG Minggao, LI Jianqiu, YANG Fuyuan
    2022, 13(2):  211-224.  doi:10.3969/j.issn.1674-8484.2022.02.001
    Abstract ( 1683 )   HTML ( 464)   PDF (4067KB) ( 811 )  

    In 2016, China formulated the roadmap for the development of hydrogen fuel cell vehicles and planned the milestones in 2020, 2025 and 2030. In the past five years, China has carried out further technology research and development, and regional demonstration and deployment of fuel cell vehicles, including the large scale application in 2022 Beijing Winter Olympics. The roadmap and milestones were revised in 2020, with technical performance modification and the timeline extending to 2035. This paper provides a comprehensive review of the above processes and summarizes the representative progress. The results show that the scale of hydrogen fuel cell vehicles and hydrogen infrastructure, as well as the key performance technical indicators of fuel cell systems and fuel cell stacks in China exceed the 2020 milestones, and some of them are close to the 2025 milestone. Taking Zhangjiakou as the representative, in the cold region rich in renewable energy resource such as wind power and solar power, the successful experience of green hydrogen production/storage/transportation and refueling as well as the promotion and deployment of hydrogen fuel cell vehicles can be used for other regions. Furthermore, the promotion of hydrogen fuel cell heavy trucks can be carried out in combination with the demonstration areas of hydrogen fuel cell vehicles in Beijing, Tianjin and Hebei Province.

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    Review and prospect of human factor testing of automotive head up displays in connected environment
    ZHAO Xiaohua, LI Xuewei, ZHANG Yu, LI Zhenlong, LI Haijian, ZHANG Jianhua
    2022, 13(2):  225-241.  doi:10.3969/j.issn.1674-8484.2022
    Abstract ( 755 )   HTML ( 266)   PDF (4473KB) ( 303 )  

    Head up displays (HUD) is an emerging way and hot topic of in-vehicle human-computer interaction in connected environment. The human factor testing of automotive HUD is a fundamental step to ensure the expected safety benefits. This paper introduces the classification, the application status, and the suggested information of HUD; The knowledge maps of human factors research of HUD are drawn to clarify the key content of previous research and its changing trend with time and space; And the paper puts forward the general testing framework of HUD from the functional scenarios, test variables, and test methods; and further discusses the human factors testing in the scenarios of navigation, safety assistance, automated driving, and other interactive assistance tasks. Finally, it summarizes the existing research gaps and put forward the future research prospects for human factors research of HUD, and provides references for the construction of the intelligent cockpit in connected environment.

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    Automotive Safety
    Design and simulation of a two-stage semi-active magneto-rheological anti-impact seat suspension system
    QI Chang, XU Bo, YU Jie, YANG Shu
    2022, 13(2):  242-249.  doi:10.3969/j.issn.1674-8484.2022.02.003
    Abstract ( 650 )   HTML ( 191)   PDF (1875KB) ( 225 )  

    A conceptual design was proposed for a seat with two-stage semi-active impact-resistant soft-landing control by using a four-degree-of-freedom lumped-parameter human body model established by authors to protect the occupants in special vehicles under explosion impacts. The validity of the model was verified through vertical impact dynamics simulation; A damper mechanical model was established based on a magnetorheological damper Hysteretic model; The correlation coefficients of the damper mechanical model were identified on a vibration test bench. The shock resistances and the vibration damping performances of the seat were investigate by a simulation model of the two-stage semi-active seat suspension system. The results show that compared with the original design, the seat designed by authors reduces the occupant’s head peak acceleration by 42.5%, with reducing the maximum effective amplitude transfer rate of the seat by 19.4%. Therefore, the seat improves comfort, reduces the occupant head injury risk, and meets the anti-explosion performance requirements.

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    Automobile reliability evaluation and automobile-enterprise operational decision system based on warranty data
    PANG Huan, SHI Dongyang, WANG Daocheng, LIU Jingyi
    2022, 13(2):  250-258.  doi:10.3969/j.issn.1674-8484.2022.02.004
    Abstract ( 598 )   HTML ( 31)   PDF (2359KB) ( 154 )  

    A system was developed to support automobile reliability-evaluation and automobile-enterprise operation-decision based on the automobile warranty data, considering customer’s actual needs. The system framework was established with functions of multi-task management and multi-dimensional analysis; External databases were designed to support data analysis and mining. The system has functions such as automatic data cleaning, failure statistical analysis, reliability evaluation, scientific decision making and data visualization. Combining the actual two-dimensional warranty data from 3 vehicle types, the system was applied to carry out fault statistical analysis, reliability evaluation, and to predict the company’s demand of maintenance spare parts in the next quarter. The results show that the relative error between the predicted value and the actual value is less than 5% when predicting spare parts of automobile enterprise in the next quarter. Therefore, the system can support the product evaluation and automobile-enterprise operational-decision.

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    Automobile crash test time-series data processing and classification method based on SSI-PSO algorithm
    LI Han, LIU Zhao, ZHU Ping
    2022, 13(2):  259-268.  doi:10.3969/j.issn.1674-8484.2022.02.005
    Abstract ( 449 )   HTML ( 61)   PDF (2184KB) ( 232 )  

    This paper investigated an optimization problem transformation and construction method for heuristic optimization algorithm to realize the category identification of dummy curve dataset from automobile crash test. A method of feature selection and classification was proposed for multi-variable time-series data in crash test based on a social spider inspired particle swarm optimization (SSI-PSO) for the feature processing and for the classification process of dummy curve data. The proposed method was tested and validated by using the dummy curve data collected from automobile crash test. The result shows that the optimal feature combination and the small-scale neural network for dummy curve classification are obtained by the proposed method. The performance of dummy curve classification model improves by 17.5% and classification accuracy reaches 96.5% based on the proposed method. Therefore, the labeling information of dummy response curve from crash test is classified effectively.

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    Test and evaluation of AEB system based on different overlap-rate collision and different light scenario
    NIU Chengyong, WU Kunlun, ZHOU Xiangxiang, SU Zhanling, HU Xiong
    2022, 13(2):  269-275.  doi:10.3969/j.issn.1674-8484.2022.02.006
    Abstract ( 601 )   HTML ( 18)   PDF (1829KB) ( 124 )  

    In order to improve the safety and stability of autonomous emergency braking (AEB) system, two commercial AEB systems were tested and evaluated. Two kinds of light environments, day and night, and two kinds of target objects (standard dummy car and cyclist model) with three different overlap-rate (50%, 75% and 100%) were set up to study the performance of the system, such as early warning time, braking efficiency, braking distance when avoiding collision, braking strategy and so on, under the condition of changing light. The results show that compared with the overlap rates scenario, the warning time of 100% overlap rate scenario is earlier, which leads to the braking time intervention lag and increasing the braking efficiency. Compared with the day test environment, the scenario with different overlap rates on the AEB system’s warning timing is significantly increased in the night scenario. The distance between the vehicle and the target vehicle decreases with the decrease of the overlap-rates and the dimming light.

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    Vibration characteristics of electric bus doors and the accelerated life test
    FENG Xiaole
    2022, 13(2):  276-281.  doi:10.3969/j.issn.1674-8484.2022.02.007
    Abstract ( 374 )   HTML ( 11)   PDF (4196KB) ( 183 )  

    The electric-bus full-driving-life test was simulated on the automotive proving ground to accelerate the full-driving-life test to ensure electric-bus safety and reliability. The time domain and frequency domain characteristics were analyzed on the city road or the stone road for the vibration acceleration signals of the electric bus door. The accelerated life test model was established based on the fatigue damage reliability theory to calculate the accelerated life testing time. The results show that the vibration frequency is mainly from 1 Hz to 20 Hz, the vibration of the top is more serious than that of bottom, and the vibration direction from large to small is vertical, longitudinal and transverse, respectively; the vibration on the stone road is much larger than that on the city road, but the vibration frequency domain distributions are basically the same. Adopting the vibration data of the front door pillar mounting point, the running time 28 800 h on the urban road is reduced to 8.11 h on the stone road to achieve the reliability verification effect of the full life.

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    Fatigue-driving detect-technology in low light environment based on facial feature points
    ZHU Yan, XIE Zhongzhi, YU Wen, LI Shusheng, ZHANG Xun
    2022, 13(2):  282-289.  doi:10.3969/j.issn.1674-8484.2022.02.008
    Abstract ( 367 )   HTML ( 15)   PDF (2245KB) ( 197 )  

    A new fatigue driving detection technology was proposed with high adaptability and recognition accuracy in low light environment. A depth vision sensor was used to obtain the driver’s driving image in real time;with extracted the face feature point data in real time through a face tracking algorithm to fit a eyes and mouth contour with a least square method.The normalized indexes were calculated for the eye and mouth opening and closing to extract six fatigue recognition feature data including the blink frequency, the average blink time, the total eye closing time, the yawning frequency, the total yawning time, and the low head up frequency. A recognition model was established based on the convolution neural network algorithm of data statistical sequence to construct a fatigue state detection system. Experiments show that in low light environment, this algorithm has a accuracy of 90% for fatigue driving recognition with a recognition time of about 130 ms.

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    Research on indirect tire pressure monitoring algorithm based on machine learning
    YU Lu, TANG Liang, WEI Lingtao, LIU Zijun
    2022, 13(2):  290-299.  doi:10.3969/j.issn.1674-8484.2022.02.009
    Abstract ( 738 )   HTML ( 32)   PDF (3111KB) ( 140 )  

    A novel tire pressure monitoring algorithm based on machine learning was presented to solve the problem that the current indirect tire pressure monitoring systems (ITPMS) achieved poor identification accuracy under complex road status and varying driving conditions, which was implemented only with the existing wheel speed sensors in vehicles. The theoretical basis of the indirect TPMS was introduced by analyzing the rigid tire model. And the inherent error generated in speed sensors was effectively removed by the recursive least square (RLS) method and accurate wheel speed signals was obtained. The features in time and frequency domains were extracted and a decision tree was used to eliminate the abnormal speed signals and a Bayesian classifier was used to identify the tire pressure conditions overall based on features of the normal speed signals. The result shows that this method in conjunction with decision trees indicates higher correctness during indirect tire pressure monitoring, where the accuracy of identification reaches 96.36% in comparison of the method only with a Bayesian classifier.

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    Adaptive automatic emergency braking control strategy based on an ESHB system
    YONG Jiawang, LI Yansong, FENG Nenglian, LIU Yahui
    2022, 13(2):  300-308.  doi:10.3969/j.issn.1674-8484.2022.02.010
    Abstract ( 470 )   HTML ( 21)   PDF (3326KB) ( 109 )  

    An adaptive-hierarchical automatic-emergency-braking (AEB) control-strategy was proposed based on the electro-servo hydraulic braking (ESHB) system to reduce the vehicle collision risk and improve vehicle safety and comfort. A vehicle model, a tire model and a road peak adhesion coefficient observer were built considering road adhesion coefficient and vehicle driving state, experiments were carried out on test bench and vehicle. The results show that the steady-state error of the road adhesion coefficient observation is less than 3 %; the steady-state tracking error of the braking pressure is less than 100 kPa; the proposed strategy avoids collision effectively within a speed of 70 km/h; in addition to comfortable, compared with the multi-level braking control-strategy with a fixed time to collision (TTC) threshold, the primary and secondary braking requests of the proposed strategy are advanced by 0.2 s and 0.1 s, respectively. Therefore, the proposed strategy could realize collision avoidance control considering adhesion condition variation.

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    Intelligent Driving and Intelligent Transportation
    Lateral velocity estimation for four-wheel-independent-drive electric vehicles based on deep reinforcement learning
    ZHENG Yangjun, HE Shuai, SHUAI Zhibin, LI Jianqiu, GAI Jiangtao, LI Yong, ZHANG Ying, LI Guohui
    2022, 13(2):  309-316.  doi:10.3969/j.issn.1674-8484.2022.02.011
    Abstract ( 377 )   HTML ( 14)   PDF (2076KB) ( 172 )  

    A lateral-velocity estimation method was proposed for an electric vehicle with four-wheel independent-drive to estimate the vehicle motion states precisely. An architecture was designed for the lateral velocity estimation method based on the deep reinforcement learning (DRL) paradigm; A DRL agent was designed with deep deterministic policy gradient (DDPG) algorithm; The actor network and the critic network of the DDPG algorithm were constructed with the recurrent neural network (RNN). The algorithm was realized and trained in Matlab/Simulink with the designed award function and training scenarios; The algorithm effectiveness was verified by the simulation of practical driving maneuvers such as double-lane changing. The results show that after 630 episodes of training and learning, the proposed method improves the estimation accuracy by 40%, compared with that of the extended Kalman filter (EKF) method. Therefore, the proposed method can be used to estimate vehicle lateral velocity in general driving scenarios.

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    Learning-based automatic driving decision-making integrated with vehicle trajectory prediction
    XU Jie, PEI Xiaofei, YANG Bo, FANG Zhigang
    2022, 13(2):  317-324.  doi:10.3969/j.issn.1674-8484.2022.02.012
    Abstract ( 550 )   HTML ( 23)   PDF (1919KB) ( 141 )  

    On the basis of considering the future trajectory of the vehicle, reinforcement learning was used to realize the decision-making problem of driving in a complex scenario. A long-term interaction trajectory prediction model of surrounding vehicles was built based on the graph structure and Long Short Term Memory (LSTM) and Rainbow DQN algorithm was used to build a behavioral decision model. In this model, the state space not only considered the current time of the vehicle information, but also considered the future trajectories of these vehicles. The corresponding reward function was designed from the perspectives of safety, comfort, driving efficiency, etc. Safety rules were set to improve the safety of selected actions. The results show that at the end of 5 s, the method with vehicle trajectory prediction has a longitudinal location error of 1.54 m with a lateral location error of 0.32 m, which are relatively accurate. Therefore, this method improves the safety and efficiency of decision-making for autonomous vehicles.

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    Pedestrian-crossing intention-recognition based on dual-stream adaptive graph-convolutional neural-network
    HU Yuanzhi, JIANG Tao, LIU Xi, SHI Youning
    2022, 13(2):  325-332.  doi:10.3969/j.issn.1674-8484.2022.02.013
    Abstract ( 560 )   HTML ( 22)   PDF (3124KB) ( 114 )  

    A recognition method was proposed to judge pedestrians’ intention to cross street for autonomous vehicles on urban roads. The method utilized a two-stream, spatiotemporally adaptive graph-convolutional neural-network (named 2s-AGCN) with linking the dynamics of pedestrian skeletons and pedestrian crossing intention; Added the adaptive graph convolutional neural-network (AGCN) structure based on the action recognition of the spatiotemporal graph convolutional neural network (ST-GCN); A dual-stream neural-network was designed in terms of the length and direction of the bones for fusing the Softmax scores output by the two networks to predict pedestrian crossing intention. Simulation experiments were carried out based on the Joint Attention in Autonomous Driving public Dataset (JAAD). The results shown that the accuracy of this 2s-AGCN method reaches 89.36%, which is 3.36% higher than the accuracy of the ST-GCN. Therefore, the recognition accuracy of this method is high.

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    Microscopic trajectory data-driven probability distribution model for weaving area of channel change
    LIU Bing, WANG Jinrui, XIE Jiming, CHEN Jinhong, DUAN Guozhong, YE Baoquan, HOU Xiaowei, PENG Bo
    2022, 13(2):  333-340.  doi:10.3969/j.issn.1674-8484.2022.02.014
    Abstract ( 593 )   HTML ( 19)   PDF (3377KB) ( 89 )  

    A meta-cellular automaton model for the intertwined region was constructed to analyze the complex vehicle lane change behavior in the weaving area of urban expressways considering the frequency distribution of lane change based on vehicle micro-trajectory data. The overhead video was used to extract the full sample of vehicle lane change information in the weaving area. Considering the actual decision time and safety priority awareness of drivers, the vehicle lane change prediction and lane change spacing in the weaving area were combined, and the lane change motivation and lane change timing rules were established respectively. Subsequently, the lane change timing decisions can be made step by step. The model was evaluated with parameters such as flow rates, density, velocities and channel change frequency distribution. The simulation results show that the constructed meta-cellular automata model is more effective, and the relative errors of flow, density and speed are 0.7%, 1.4% and 1.6%, respectively, and the errors of the number of lane changes in different directions are 2.97%~22.98%, and the distribution of lane changes is basically consistent with the measured data, which can effectively simulate the bottleneck phenomenon in the interweaving area, and reflect the real demand for lane changes and describe the actual operation state.

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    Automotive Energy Efficiency and Environment Protection
    Construction of driving cycle of city bus line in Xi’an based on clustering and Markov chain method
    LI Yaohua, SHAO Pandeng, ZHAI Dengwang, REN Tianyuan, SONG Weiping, LIU Yang, ZHAO Chenghui
    2022, 13(2):  341-349.  doi:10.3969/j.issn.1674-8484.2022.02.015
    Abstract ( 484 )   HTML ( 13)   PDF (2109KB) ( 62 )  

    A driving cycles of Xi’an city bus were constructed by clustering and Markov chain method to construct driving conditions that reflect the local vehicle driving characteristics for a specific area. The number of clusters and characteristic parameters in clustering were determined, and the method to determine the length of driving cycle by Markov chain was proposed. The specific energy consumption per mileage was defined as the criterion to select typical driving cycle among 50 candidate driving cycles. The results show that, compared with the clustering method and the V-A matrix method, the driving conditions constructed based on the clustering and Markov chain method have the smallest deviation from the sample data, the average deviation rate is 1.17%, and the difference in energy consumption per 100 kilometers is the smallest, the deviation rate is 0.069%. The driving cycle constructed by clustering and Markov chain method shows higher accuracy and can reflect the actual driving conditions better.

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    Coordinated control of FCV braking energy recovery considering dynamic load shedding characteristics of PEMFC
    LU Dagang, YI Fengyan, HU Donghai, CHENG Shan
    2022, 13(2):  350-357.  doi:10.3969/j.issn.1674-8484.2022.02.016
    Abstract ( 461 )   HTML ( 13)   PDF (4009KB) ( 275 )  

    A control strategy of braking energy recovery was investigated for fuel cell vehicle (FCV) with proton exchange membrane fuel cells (PEMFC) based on the dynamic load shedding characteristics of PEMFC to make the FCV recover the braking energy to a greater extent. The dynamic characteristic model of FCV was established, and a coordination-based braking energy recovery control strategy was proposed considering the dynamic load shedding characteristics of PEMFC. The results show that compared with the rule-based control strategy, the SOC of the power battery can be improved by 1.3% under city conditions and 2.0% under high speed conditions with using coordination control strategy. The corresponding maximum impact is reduced by 3.2% and 2.1%, respectively. Therefore, fuel cell vehicles recycle more braking energy while improving braking comfort to a certain extent.

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    Energy management strategy of plug-in hybrid electric bus based on 6σ Design
    LAI Huiping, HOU Liang, WANG Shaojie, LIU Qiangsheng
    2022, 13(2):  358-367.  doi:10.3969/j.issn.1674-8484.2022.02.017
    Abstract ( 406 )   HTML ( 77)   PDF (2613KB) ( 88 )  

    A design method of energy management strategy for plug-in hybrid electric bus (PHEB) was proposed based on the 6σ Design (Design For Six Sigma, DFSS) method to improve urban PHEB to adapted to random mass, different road slopes or/and different driving cycles. The off-line process used the 6σ-Design and the equivalent fuel consumption control strategy (ECMS), taken the random passenger load as noise factor, to obtain robust control parameters that can resist random load changes. And adaptive control was achieved by an online identification model with k Nearest Neighbor (KNN) method for driving conditions on complex roads. The results show that the 100-km fuel consumption of the proposed method is reduced by 2.64 L on average compared with the rule-based control strategy. Therefore, this method improves the adaptability of hybrid urban buses under different driving conditions.

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    Effect of internal structure combination optimization on cooling performance of cold plate
    ZHANG Furen, LU Fu, WU Bo, XIAO Kang
    2022, 13(2):  368-377.  doi:10.3969/j.issn.1674-8484.2022.02.018
    Abstract ( 551 )   HTML ( 15)   PDF (4745KB) ( 110 )  

    A liquid cooling plate with uniform straight channel and array fins was proposed to optimize the cooling performance of battery pack. Based on the evaluation method of the computational fluid dynamics (CFD), the cooling performance of the established model was analyzed and optimized. It was investigated under 5 C discharge condition that the effects of different combination forms of channels and array fins in the liquid cooling plate, inlet and outlet width, fin size and the spacing between upper and lower cooling tanks on battery thermal management. The results show that the use of channels on both sides of the middle fin can change the maximum temperature, the average temperature and temperature difference, and the inlet and outlet width significantly change the inlet and outlet pressure drop. Compared with the original model, when the mass flow rate is 0.5 g/s, the optimization model meets the maximum temperature of 39.99 °C, the average temperature is reduced by 0.93 °C, while the temperature standard deviation and inlet and outlet pressure drop are reduced by 0.09 °C and 494.68 Pa, respectively; the coolant mass flow rate has a certain degree of influence on the heat dissipation performance of the liquid cold plate.

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    Layout optimization of cooling modules in a commercial vehicle engine compartment
    ZHOU Donghui, LUO Renhong, WANG Zhifeng
    2022, 13(2):  378-385.  doi:10.3969/j.issn.1674-8484.2022.02.019
    Abstract ( 394 )   HTML ( 15)   PDF (2717KB) ( 104 )  

    A method for parameterizing the position of the cooling module in the engine front compartment was proposed to improve the water temperature of a commercial vehicle engine and the wind resistance of the whole vehicle. Combined with the 1 D/3 D computing platform, a calculation model of the engine compartment of the prototype vehicle was established. Based on the calculation model, a four-factor and four-level orthogonal test with the distance between the intercooler, the radiator, the cooling fan and the engine body in the engine compartment was used as the optimization variable. The engine outlet water temperature and the wind resistance coefficient of the whole vehicle are calculated, and the orthogonal test results were analyzed by the range method to obtain the primary and secondary relationship of each factor and level on the engine cooling effect and aerodynamic resistance, so as to determine the optimal combination scheme. The results show that the optimal solution is 2.3 ℃ lower than the original engine outlet water temperature, and 9.03 counts lower than the original wind resistance coefficient. Therefore, this optimization method can effectively reduce the engine outlet water temperature and the wind resistance of the whole vehicle.

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    Effect of different additives on performance of natural gas HCCI engine based on simulation
    SUN Nannan, WANG Xiaoyan, WANG Dan, JIA Demin
    2022, 13(2):  386-396.  doi:10.3969/j.issn.1674-8484.2022.02.020
    Abstract ( 364 )   HTML ( 15)   PDF (2658KB) ( 85 )  

    Aiming at the misfire problem of natural gas mixed homogeneous compression ignition (HCCI) engine at low load, a three-dimensional model of the modified single cylinder engine was built to study the effect of hydrogen (H2), ozone (O3) and dimethyl ether (DME) as the additives on combustion characteristics and combustion-supporting mechanism. The results show that the three additive components can change the autoignition characteristics of natural gas-air mixture, enhance the ignition ability, and advance the combustion phase, all of which can realize the expansion of natural gas HCCI to the low load range. Radical OH is an important component to enhance the combustion of natural gas HCCI. The combustion-supporting mechanism of H2 and O3 is to increase the concentration of free-radical OH and accelerate the reaction rate of methane consumption, while combustion-supporting mechanism of DME is to ignite natural gas. H2 is an excellent additive to extend the low load operation boundary of natural gas HCCI engine.

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    Comparative test of influence of load conditions on specific emissions of heavy-duty vehicles with different calculation methods
    ZHANG Fan, LI Liang, YU Jintao
    2022, 13(2):  397-405.  doi:10.3969/j.issn.1674-8484.2022.02.021
    Abstract ( 428 )   HTML ( 13)   PDF (3700KB) ( 88 )  

    The on-board emission analysis system was used to carry out the actual road emission tests with different load ratios to quantitatively evaluate the variation of emission per unit mileage and emission per unit engine work with load ratio of heavy-duty vehicles, using 6 vehicles of stage VI of the National Standard and 2 vehicles of stage V of the National Standard. The results show that the load correction coefficient of particulate number (PN) emission per unit mileage of stage VI vehicles increases significantly with the increase of load ratio, and the slope of the fitted curve is 0.011 9, while the slope of stage 5 vehicle is 0.001 2, which is much lower than that of stage VI vehicle. With the increase of the load ratio, the PN and NOx emissions per unit engine work increase, CO2 emissions remain unchanged, and CO emissions decrease. When driving the same mileage, the larger the load ratio, the higher the PN emission level per unit vehicle mass, and the lower the emission levels of CO, CO2 and NOx. Under the condition of high load ratio, the National VI vehicles show high PN peaks in the initial urban stage and the final high-speed stage.

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