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Received：19 June 2018，
Revised：2018-8-7，
Published：16 February 2019
移动端阅览
目的
2
决策系统是无人驾驶技术的核心研究之一。已有决策系统存在逻辑不合理、计算效率低、应用场景局限等问题，因此提出一种动态环境下无人驾驶路径决策仿真。
方法
2
首先，基于规则模型构建适于无人驾驶决策系统的交通有限状态机；其次，针对交通动态特征，提出基于统计模型的动态目标路径算法计算状态迁移风险；最后，将交通状态机和动态目标路径算法有机结合，设计出一种基于有限状态机的无人驾驶动态目标路径模型，适用于交叉口冲突避免和三车道换道行为。将全速度差连续跟驰模型运用到换道规则中，并基于冲突时间提出动态临界跟车距离。
结果
2
为验证模型的有效性和高效性，对交通环境进行虚拟现实建模，模拟交叉口通行和三车道换道行为，分析文中模型对车流量和换道率的影响。实验结果显示，在交叉口通行时，自主车辆不仅可以检测冲突还可以根据风险评估结果执行安全合理的决策。三车道换道时，自主车辆既可以实现紧急让道，也可以通过执行换道达成自身驾驶期望。通过将实测数据和其他两种方法对比，当车流密度在0.20.5时，本文模型的平均速度最高分别提高32 km/h和22 km/h。当车流密度不超过0.65时，本文模型的换道成功率最高分别提升37%和25%。
结论
2
实验结果说明本文方法不仅可以在动态城区环境下提高决策安全性和正确性，还可以提高车流量饱和度，缓解交通堵塞。
Objective
2
Driverless technology is an essential part of intelligent transportation systems
such as environmental information perception
intelligent planning
and multilevel auxiliary driving. This technology reduces driver's work intensification and prevents accidents. With the development of artificial intelligence
autonomous vehicles have attracted considerable attention in the industry and academia in recent years. In addition
a decision-making system is a core research of driverless technology. The reduction on the number of road accidents is of paramount societal importance
and increasing research efforts have been devoted to decision-making systems within the past few years. Conducting human-like decisions with other encountered vehicles in complex traffic scenarios causes great challenges to autonomous vehicles. The research on autonomous driving decision systems has important theoretical and practical values to improve the level of intelligent vehicles and intelligent transportation systems. However
the current decision-making system has several limitations
such as unreasonable logic
large computational complexity
and limited application scene
due to the uncertainty and randomness of the driving behavior of surrounding vehicles. To solve these problems
this study constructs a finite-state machine-based decision-making system for the safety driving of autonomous vehicles in dynamic urban traffic environments. This study mainly investigates the passage of vehicles through intersections and their changing of lanes
which are the core issues of decision-making systems.
Method
2
The driver's behavior at a certain period of time is determined based on the current traffic condition and risk perception. We define the primary state of the vehicle based on the driving range of the autonomous vehicle
such as driving at the intersection
driving in the driveway
and approaching the crossroads. Each primary state includes many secondary states. For example
a vehicle at crossroads may turn or keep straight. Combined with the original finite-state machine theory
a suitable traffic state machine (TSM) for intelligent systems is proposed. Considering the complexity and diversity of traffic environment
a dynamic target path (DTP) algorithm is proposed to improve the feasibility of the decision system. Combined with the TSM and DTP algorithm
we propose a DTP model based on finite-state machine for the decision system and analyze the importance of the model. For complex and diverse traffic environment
intelligent vehicles only focus on their own driving information and ignore the state of other vehicles
which cause considerable risks. Thus
we divide the awareness and conflict areas for each autonomous vehicle. The perceived range of autonomous vehicles at the crossroads is defined as the awareness area
and the reachable range of autonomous vehicles is called the conflict area. The perception area of vehicles in the driveway is defined as the consciousness area
and the range of interaction between autonomous and surrounding vehicles is defined as the conflict area. A reasonable decision can effectively reduce the probability of accidents in conflict areas. We use the DTP algorithm to calculate the risk of decision making in restricting vehicle behavior. A fixed follow-up distance cannot consider the influence of speed. Thus
this study proposes a dynamic critical follow-up distance
which reduces the collision with preceding vehicle while following the vehicle. Furthermore
a full velocity difference model is used to avoid collision with the front vehicle of the target lane during lane change under different scenarios.
Results
2
We repeatedly perform experiments in different scenarios through the Unity 3D engine to verify the effectiveness of the model and algorithm. In the first experiment
we simulate a scene of an autonomous vehicle driven at a crossroad. The second experiment simulates the responses of autonomous vehicles to emergencies. The third experiment simulates the changing of lanes of autonomous vehicles in reaching their destinations. The fourth experiment simulates the changing of lanes of autonomous vehicles in increasing their speed. We simulate the lane changing behavior of autonomous vehicles during foggy days to verify that the experimental results are unaffected by poor weather conditions. Experiments show that autonomous vehicles not only can meet the driving expectation but also ensure driving safety during poor weather conditions. Experimental results show that the driving intentions of other vehicles can be obtained and autonomous vehicles can make correct decisions based on the potential risk of intersection and current traffic environment. Autonomous vehicles can change lanes based on their driving demand when driving on the driveway. In case of emergencies
the autonomous vehicle considers the special vehicle as a dynamic obstacle. After yielding the right-of-way to emergency vehicles
the autonomous vehicle returns to the original lane to continue driving. To prove that the proposed method can improve the traffic flow efficiency
the proposed model is compared with other models. Results demonstrate that the difference among the three models is uncertain when the vehicle density is small. However
the average speed of the model is increased at most by 32 km/h and 22 km/h when the vehicle density is greater than 0.2 and less than 0.5
respectively. The success rate of lane changing in this model is approximately increased at most by 37 percentage points and 25 percentage points when the density of vehicles is less than 0.65
respectively.
Conclusions
2
The proposed algorithm not only improves the safety and accuracy of decision making in dynamic urban traffic environment but also helps improve traffic flow saturation and reduces traffic flow. In addition
various traffic environments can be modeled by our simulation framework. Although the proposed model and algorithm are relatively simple
the assessment of potential risks can meet the planning time of autonomous driving. Our work provides the rules for the decision making in autonomous driving and several references for the development of intelligent transportation systems. However
the influence of vehicle types
trajectory
and road width on decision making are ignored. In the future
we will improve the current work and provide a complete and reasonable framework for automatic driving decision systems.
Wang M L. Local collision-free path planning and path tracking control technologies in the autonomous navigation of intelligent vehicle[D]. Hefei: Hefei University of Technology, 2013. http://cdmd.cnki.com.cn/Article/CDMD-10359-1014120914.htm .
汪明磊.智能车辆自主导航中避障路径规划与跟踪控制研究[D].合肥: 合肥工业大学, 2013.
Gipps P G. A model for the structure of lane-changing decisions[J]. Transportation Research Part B:Methodological, 1986, 20(5):403-414.[DOI:10.1016/0191-2615(86)90012-3]
Furda A, Vlacic L. Enabling safe autonomous driving in real-world city traffic using multiple criteria decision making[J]. IEEE Intelligent Transportation Systems Magazine, 2011, 3(1):4-17.[DOI:10.1109/MITS.2011.940472]
Gindele T, Jagszent D, Pitzer B, et al. Design of the planner of team AnnieWAY's autonomous vehicle used in the DARPA Urban Challenge 2007[C]//Intelligent Vehicles Symposium. Eindhoven, Netherlands: IEEE, 2008: 1131-1136.[ DOI: 10.1109/IVS.2008.4621268 http://dx.doi.org/10.1109/IVS.2008.4621268 ]
Ziegler J, Bender P, Schreiber M, et al. Making bertha drive-an autonomous journey on a historic route[J]. IEEE Intelligent Transportation Systems Magazine, 2014, 6(2):8-20.[DOI:10.1109/MITS.2014.2306552]
Gong J W, Xu Y Z, Lu C, et al. Decision-making model of overtaking behavior for automated driving on freeways[C]//Proceedings of 2016 IEEE International Conference on Vehicular Electronics and Safety. Beijing, China: IEEE, 2016.[ DOI: 10.1109/ICVES.2016.7548162 http://dx.doi.org/10.1109/ICVES.2016.7548162 ]
Brechtel S, Gindele T, Dillmann R. Probabilistic decision-making under uncertainty for autonomous driving using continuous POMDPs[C]//Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems. Qingdao, China: IEEE, 2014.[ DOI: 10.1109/ITSC.2014.6957722 http://dx.doi.org/10.1109/ITSC.2014.6957722 ]
Bouton M, Cosgun A, Kochenderfer M J. Belief state planning for autonomously navigating urban intersections[C]//2017 IEEE Intelligent Vehicles Symposium. Los Angeles, CA, USA: IEEE, 2017: 825-830.[ DOI: 10.1109/IVS.2017.7995818 http://dx.doi.org/10.1109/IVS.2017.7995818 ]
Noh S, An K. Decision-making framework for automated driving in highway environments[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(1):58-71.[DOI:10.1109/TITS.2017.2691346]
Chowdhury D, Wolf D E, Schreckenberg M. Particle hopping models for two-lane traffic with two kinds of vehicles:Effects of lane-changing rules[J]. Physica A:Statistical Mechanics and Its Applications, 1997, 235(3-4):417-439.[DOI:10.1016/S0378-4371(96)00314-7]
Wang J, Ding J X, Shi Q, et al. Lane-changing behavior and its effect on energy dissipation using full velocity difference model[J]. International Journal of Modern Physics C, 2016, 27(2):#1650013.[DOI:10.1142/S0129183116500133]
Aki M, Rojanaarpa T, Nakano K, et al. Road surface recognition using laser radar for automatic platooning[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10):2800-2810.[DOI:10.1109/TITS.2016.2528892]
Garcia F, Martin D, de la Escalera A, et al. Sensor fusion methodology for vehicle detection[J]. IEEE Intelligent Transportation Systems Magazine, 2017, 9(1):123-133.[DOI:10.1109/MITS.2016.2620398]
Tomar R S, Verma S, Tomar G S. SVM based trajectory predictions of lane changing vehicles[C]//Proceedings of 2011 International Conference on Computational Intelligence and Communication Networks. Gwalior, India: IEEE, 2011: 716-721.[ DOI: 10.1109/CICN.2011.156 http://dx.doi.org/10.1109/CICN.2011.156 ]
Houenou A, Bonnifait P, Cherfaoui V, et al. Vehicle trajectory prediction based on motion model and maneuver recognition[C]//Proceedings of 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo, Japan: IEEE, 2013: 4363-4369.[ DOI: 10.1109/IROS.2013.6696982 http://dx.doi.org/10.1109/IROS.2013.6696982 ]
Woo H, Ji Y, Kono H, et al. Lane-change detection based on vehicle-trajectory prediction[J]. IEEE Robotics and Automation Letters, 2017, 2(2):1109-1116.[DOI:10.1109/LRA.2017.2660543]
Xie G T, Gao H B, Qian L J, et al. Vehicle trajectory prediction by integrating physics- and maneuver-based approaches using interactive multiple models[J]. IEEE Transactions on Industrial Electronics, 2018, 65(7):5999-6008.[DOI:10.1109/TIE.2017.2782236]
Shaout A, Colella D, Awad S. Advanced driver assistance systems - past, present and future[C]//Proceedings of the 7th International Computer Engineering Conference. Giza, Egypt: IEEE, 2011: 72-82.[ DOI: 10.1109/ICENCO.2011.6153935 http://dx.doi.org/10.1109/ICENCO.2011.6153935 ]
Kiefer R J, LeBlanc D J, Flannagan C A. Developing an inverse time-to-collision crash alert timing approach based on drivers' last-second braking and steering judgments[J]. Accident Analysis&Prevention, 2005, 37(2):295-303.[DOI:10.1016/j.aap.2004.09.003]
Jiang R, Wu Q S, Zhu Z J. Full velocity difference model for a car-following theory[J]. Physical Review E, 2001, 64(1 Pt 2):#017101.[DOI:10.1103/PhysRevE.64.017101]
Helbing D, Tilch B. Generalized force model of traffic dynamics[J]. Physical Review E, 1998, 58(1):133-138.[DOI:10e.1103/PhysRevE.58.133]
Kondoh T, Yamamura T, Kitazaki S, et al. Identification of visual cues and quantification of drivers' perception of proximity risk to the lead vehicle in car-following situations[J]. Journal of Mechanical Systems for Transportation and Logistics, 2008, 1(2):170-180.[DOI:10.1299/jmtl.1.170]
Ohta H. Individual differences in driving distance headway[C]//Vision in Vehicles Ⅳ. Proceedings of the Fourth International Conference on Vision in Vehicles, University of Leiden, the Netherlands, 27-29 August, 1991. Netherlands: North-Holland, 1993: 360-360. https://trid.trb.org/view.aspx?id=383541 .
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