In order to ensure that the autonomous vehicle can predict and taking actions to avoid the collision in time when facing the obstacles with intersection collision risk, an intersection collision risk prediction system is proposed in this paper, and two kinds of active obstacle avoidance strategies are designed according to the system: braking strategy and steering strategy. The position information of the obstacle is predicted by Fractional extended Kalman filter, the collision risk rate is determined by the time difference between the vehicle and the obstacle through the intersection point, and a neural network is trained to quickly give the collision risk of the vehicle and the obstacle. Braking strategy and steering strategy are formulated according to collision risk, the braking deceleration and Sigmoid path parameters are given. Finally, the simulation results of PreScan and MATLAB show that the collision risk prediction system can accurately predict the collision between vehicles and obstacles, the braking and steering strategies can effectively avoid the collision.
XuanRHLiZWangYQ, et al. Path planning of intelligent vehicle based on optimized A* algorithm. In: Chinese automation congress (CAC), Xian, China, November 30 to December 2, 2018. New York, NY: IEEE, pp. 526–531.
2.
EnacheNMNettoMMammarS, et al. Driver steering assistance for lane departure avoidance. Control Eng Pract2009; 17: 642–651.
3.
HuHGaoZYuZ, et al. An experimental driving simulator study of unintentional lane departure. Adv Mech Eng2017; 9: 1687814017726290.
4.
ChenMZhanXZhangX, et al. Localisation-based autonomous vehicle rear-end collision avoidance by emergency steering. IET Intell Transp Syst2019; 13: 1078–1087.
5.
WuBZhuXShenJ, et al. A study on risk assessment algorithm based on natural driving data. Automot Eng2017; 39: 907–914.
6.
WuQLiuWDGuoSY, et al. Research on lane-change strategy with real-time obstacle avoidance function. IEEE Access2020; 8: 211255–211268.
7.
LuBHeHWYuHL, et al. Hybrid path planning combining potential field with sigmoid curve for autonomous driving. Sensors2020; 20: 22.
8.
HuangXJZhangWZLiP. A path planning method for vehicle overtaking maneuver using sigmoid functions. In: 10th IFAC symposium on intelligent autonomous vehicles IAV, Gdansk, Poland, July 3 to 5, 2017, IFAC papersonline. Amsterdam: Elsevier, pp. 422–427.
9.
WuZYangJHuangL. Study on the collision avoidance strategy at unsignalized intersection based on PreScan simulation. In: 13th COTA international conference of transportation professionals (CICTP), Shenzhen, China, August 13 to 16, 2013, pp. 1315–1321. Amsterdam, Netherlands: Elsevier.
10.
BasmaFTachwaliYRefaiHH, et al. Intersection collision avoidance system using infrastructure communication. In: 14th International IEEE conference on intelligent transportation systems (ITSC), Campus George Washington University, Washington, DC, USA, October 5 to 7, 2011. New York, NY: IEEE, pp. 422–427.
11.
YangWWanBQuXL.A forward collision warning system using driving intention recognition of the front vehicle and V2V communication. IEEE Access2020; 8: 11268–11278.
12.
BaekMJeongDChoiD, et al. Vehicle trajectory prediction and collision warning via fusion of multisensors and wireless vehicular communications. Sensors2020; 20: 26.
13.
KimYRyuJBaekM, et al. Development and performance analysis of bicycle collision avoidance (BCA) algorithms by using PreScan and MATLAB. In: 18th IEEE international conference on intelligent transportation systems, Spain, September 15 to 18, 2015. IEEE, pp. 1506–1511. New York, NY: IEEE.
14.
WangMYiJChenX, et al. Spatial and temporal distribution analysis of traffic accidents using GIS-based data in Harbin. J Adv Transp2021; 2021: 1–10.
15.
AlahiAGoelKRamanathanV, et al. Social LSTM: Human trajectory prediction in crowded spaces. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), Seattle, WA, USA, June 27 to 30, 2016. New York, NY: IEEE, pp. 961–971.
16.
ZhangSAbdel-AtyMYuanJ, et al. Prediction of pedestrian crossing intentions at intersections based on long short-term memory recurrent neural network. Transp Res Rec2020; 2674: 57–65.
17.
YeNZhangYWangR, et al. Vehicle trajectory prediction based on Hidden Markov Model. Ksii Trans Internet Info Syst2016; 10: 3150–3170.
18.
QiaoS-JHanNZhuX-W, et al. A dynamic trajectory prediction algorithm based on Kalman filter. Acta Electronica Sin2018; 46: 418–423.
19.
LiuTYChengSSWeiYH, et al. Fractional central difference Kalman filter with unknown prior information. Signal Process2019; 154: 294–303.
ManjunathALiuYHenriquesB, et al. Radar based object detection and tracking for autonomous driving. In: IEEE MTT-S international conference on microwaves for intelligent mobility (ICMIM), Munich, Germany, April 15 to 17, 2018. New York, NY: IEEE. pp. 126–129.
22.
ZhangSWSimkaniMZadehMH, et al. Automatic vehicle parallel parking design using fifth degree polynomial path planning. In: IEEE 74th vehicular technology conference (VTC), San Francisco, CA, USA, September 5 to 8, 2011. New York, NY: IEEE.
23.
ElbanhawiMSimicMJazarR. Improved manoeuvring of autonomous passenger vehicles: Simulations and field results. J Vib Control2017; 23: 1954–1983.
24.
XiaoyuGPengWNingC, et al. Research on vehicle emergency steering obstacle avoidance system based on generalized sigmoid function. Automob Technol2021; s: 7–13. DOI: 10.19620/j.cnki.1000-3703.20210351.
25.
LaghmaraHBoudaliM-TLaurainT, et al. Obstacle avoidance, path planning and control for autonomous vehicles. In: 30th IEEE intelligent vehicles symposium, IV 2019, Paris, France, June 9 to June 12, 2019. New York, NY: IEEE, pp. 529–534.
26.
YiBGottschlingSFerdinandJ, et al. Real time integrated vehicle dynamics control and trajectory planning with MPC for critical maneuvers. In: IEEE intelligent vehicles symposium (IV), Gothenburg, Sweden, June 19 to 22, 2016. New York, NY: IEEE, pp. 584–589.
