Preceding vehicles are easily lost due to the limited view of onboard sensors on roads with large curvature, resulting in the functional failure of the environmental perception system. This makes intelligent vehicles unable to obtain reliable information to make reasonable decisions, threatening the safety of car-following. Therefore, this paper proposes a method to improve car-following safety on blind curved roads. By analyzing the cautious mindset of drivers during cornering, an improved Markov model based on long-term prediction decay is proposed, combined with a Long Short-Term Memory (LSTM) model, to effectively predict the state of the preceding vehicle and address the intended functional safety issues arising from sensor functional failures. Subsequently, different strategies are designed for various states of the preceding vehicle, such as acceleration and deceleration, and a sensor field-of-view distance model is established to provide effective distance parameters for vehicle control. Notably, the proposed strategy has undergone rigorous simulation and hardware-in-the-loop (HiL) tests, proving its effectiveness in improving car-following safety on large curvature curves and instilling confidence in its potential.
RorizRCabralJGomesT.Automotive LiDAR technology: a survey. IEEE Trans Intell Transp Syst2021; 23(7): 6282–6297.
2.
YeongDVelasco-HernandezGBarryJ, et al. Sensor and sensor fusion technology in autonomous vehicles: a review. Sensors2021; 21(6): 2140.
3.
XingLHeJAbdel-AtyM, et al. Time-varying analysis of traffic conflicts at the upstream approach of toll plaza. Accid Anal Prev2020; 141: 1–14.
4.
DadashovaBRamírezBMcWilliamsJ, et al. The identification of patterns of interurban road accident frequency and severity using road geometry and traffic indicators. Transp Res Procedia2016; 14: 4122–4129.
5.
CampbellSO’MahonyNKrpalcovaL, et al. Sensor technology in autonomous vehicles: a review. In: 2018 29th Irish signals and systems conference (ISSC), Belfast, UK, 21–22 June 2018, pp.1–4. New York: IEEE.
6.
Van BrummelenJO’BrienMGruyerD, et al. Autonomous vehicle perception: the technology of today and tomorrow. Transp Res Part C Emerg Technol2018; 89: 384–406.
7.
KordaniAMolanA.The effect of combined horizontal curve and longitudinal grade on side friction factors. KSCE J Civ Eng2015; 19(1): 303–310.
8.
EftekharzadehSFKhodabakhshiA.Safety evaluation of highway geometric design criteria in horizontal curves at downgrades. Int J Civ Eng2014; 12(3): 326–332.
9.
WangBHallmarkSSavolainenP, et al. Crashes and near-crashes on horizontal curves along rural two-lane highways: analysis of naturalistic driving data. J Safety Res2017; 63(1): 163–169.
10.
FenicheMMazriT. Lane detection and tracking for intelligent vehicles: a survey. In: 2019 international conference of computer science and renewable energies (ICCSRE), Agadir, Morocco, 22–24 July 2019, pp.1–4. New York: IEEE.
11.
NaroteSPBhujbalPNNaroteAS, et al. A review of recent advances in lane detection and departure warning system. Pattern Recognit2018; 73: 216–234.
12.
AlmotairiKH.Hybrid adaptive method for lane detection of degraded road surface condition. J King Saud Univ Comput Inf Sci2022; 34(8): 5261–5272.
13.
HeinzlerRSchindlerPSeekircherJ, et al. Weather influence and classification with automotive lidar sensors. In: 2019 IEEE intelligent vehicles symposium, Paris, France, 9–12 June 2019, pp.1527–1534. New York: IEEE.
14.
KutilaMPyykonenPHolzhuterH, et al. Automotive LiDAR performance verification in fog and rain. In: 2018 21st international conference on intelligent transportation systems (ITSC), Maui, HI, USA, 4–7 November 2018, pp.1695–1701. New York: IEEE.
15.
MuckenhuberSMuseljicEStettingerG.Performance evaluation of a state-of-the-art automotive radar and corresponding modeling approaches based on a large labeled dataset. J Intell Transp Syst2022; 26(6): 655–674.
16.
PappalardaGCaponettoRVarricaR, et al. Assessing the operational design domain of lane support system for automated vehicles in different weather and road conditions. J Traffic Transp Eng2021; 9(4): 631–644.
17.
SoJHwangboJKimSH, et al. Analysis on autonomous vehicle detection performance according to various road geometry settings. J Intell Transp Syst2023; 27(3): 384–395.
18.
WuPSongLMengX.Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: a comparison between rural roads and urban roads. J Safety Res2021; 79: 199–210.
19.
SongLLiYFanW, et al. Modeling pedestrian-injury severities in pedestrian-vehicle crashes considering spatiotemporal patterns: insights from different hierarchical Bayesian random-effects models. Anal Methods Accid Res2020; 28: 100137.
20.
LymYChenZ.Influence of built environment on the severity of vehicle crashes caused by distracted driving: a multi-state comparison. Accid Anal Prev2021; 150: 105920.
21.
SongLFanW.Combined latent class and partial proportional odds model approach to exploring the heterogeneities in truck-involved severities at cross and T-intersections. Accid Anal Prev2020; 144: 105638.
22.
LanJZhaoDTianD. Robust cooperative adaptive cruise control of vehicles on banked and curved roads with sensor bias. In: 2020 American control conference (ACC), Denver, CO, USA, 1–3 July 2020, pp.2276–2281. New York: IEEE.
23.
MalekzadehMPapamichailIPapageorgiouM, et al. Optimal internal boundary control of lane-free automated vehicle traffic. Transp Res Part C Emerg Technol2021; 126: 103060.
24.
KarafyllisITheodosisDPapageorgiouM. Lyapunov-based two-dimensional cruise control of autonomous vehicles on lane-free roads. In: 2021 60th IEEE conference on decision and control (CDC), Austin, TX, USA, 14–17 December 2021, pp.2683–2689. New York: IEEE.
25.
LinigerADomahidiAMorariM.Optimization-based autonomous racing of 1: 43 scale RC cars. Optim Control Appl Methods2015; 36(5): 628–647.
26.
LevyRHaddadJ.Cooperative path and trajectory planning for autonomous vehicles on roads without lanes: a laboratory experimental demonstration. Transp Res Part C Emerg Technol2022; 144: 103813.
27.
ZhaiCWuWXiaoY.Cooperative car-following control with electronic throttle and perceived headway errors on gyroidal roads. Appl Math Model2022; 108: 770–786.
28.
NarmadhaSVijayakumarV.Spatio-temporal vehicle traffic flow prediction using multivariate CNN and LSTM model. Mater Today Proc2023; 81: 826–833.
29.
CaiBZhaoLWangQ, et al. A strategy of vehicle following on slope road at night considering the safety of the intended functionality. Phys A2023; 624: 128951.
30.
KanellaidisG.Factors affecting drivers’ choice of speed on roadway curves. J Safety Res1995; 26(1): 49–56.
31.
NicholsonA.Superelevation, side friction, and roadway consistency. J Transp Eng1998; 124(5): 411–418.
32.
MourantRRGeZ.Measuring attentional demand in a virtual environments driving simulator. Proc Hum Factors Ergon Soc Annu Meet1997; 41: 1268–1272.
33.
ShinarD.Looks are (almost) everything: where drivers look to get information. Hum Factors2008; 50(3): 380.
34.
NiuJLuJXuM, et al. Robust lane detection using two-stage feature extraction with curve fitting. Pattern Recognit2015; 59: 225–233.
35.
LiSEDengKLiK, et al. Terminal sliding mode control of automated car-following system without reliance on longitudinal acceleration information. Mechatronics2015; 30: 327–337.
36.
ChuZSunYWuC, et al. Active disturbance rejection control applied to automated steering for lane keeping in autonomous vehicles. Control Eng Pract2018; 74: 13–21.
37.
LiMLiZCaoZ.Enhancing car-following performance in traffic oscillations using expert demonstration reinforcement learning. IEEE Trans Intell Transp Syst2024; 25: 7751–7766.
38.
DaivesRHeHTHuiF.A multiscale framework for capturing oscillation dynamics of autonomous vehicles in data-driven car-following models. IEEE Trans Intell Transp Syst2024; 11(25): 18224–18235.
