In this article, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. This method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the Unscented Kalman Filter fusion is compared to that of the Extended Kalman Filter fusion showing its superiority. The Unscented Kalman Filter has outperformed the Extended Kalman Filter on all test cases and all the state variable levels (−24% average Root Mean Squared Error). The employed fusion technique shows how outstanding is the improvement in tracking performance compared to the use of a single device (−29% Root Mean Squared Error with lidar and −38% Root Mean Squared Error with radar).
FaragW.Traffic signs classification by deep learning for advanced driving assistance systems. Intel Decis Tech2019; 13(3): 215–231.
2.
FaragWSalehZ. Road lane-lines detection in real-time for advanced driving assistance systems. In: International conference on innovation and intelligence for informatics, computing, and technologies (3ICT), Bahrain18–20 November 2018. New York: IEEE.
3.
FaragW.A comprehensive real-time road-lanes tracking technique for autonomous driving. Int J Comput Digital Syst2020; 9(3): 349–362.
4.
FaragWSalehZ. Behavior cloning for autonomous driving using convolutional neural networks. In: International conference on innovation and intelligence for informatics, computing, and technologies (3ICT), Bahrain18–20 November 2018. New York: IEEE.
5.
FaragW.Recognition of traffic signs by convolutional neural nets for self-driving vehicles. Int J Knowle Based Intel Eng Syst2018; 22(3): 205–214.
6.
FaragWSalehZ. Tuning of PID track followers for autonomous driving. In: International conference on innovation and intelligence for informatics, computing, and technologies (3ICT), Bahrain18–20 November 2018. New York: IEEE.
7.
FaragW.Complex trajectory tracking using PID control for autonomous driving. Int J Intel Transp Syst Res2020; 18: 356–366.
8.
FaragW.Complex-track following in real-time using model-based predictive control. Int J Intel Transp Syst Res. Epub ahead of print 12 June 2020. DOI: 10.1007/s13177-020-00226-1.
9.
FaragWSalehZ. An advanced road-lanes finding scheme for self-driving cars. In: 2nd smart cities symposium (SCS’19), Bahrain, 24–26 March 2019. New York: IEEE.
10.
FaragW.Safe-driving cloning by deep learning for autonomous cars. Int J Adv Mechatron Syst2019; 7(6): 390–397.
11.
FaragW.Cloning safe driving behavior for self-driving cars using convolutional neural networks. Recent Patent Comput Sci2019; 12: 120–127.
12.
FaragWSalehZ. An advanced vehicle detection and tracking scheme for self-driving cars. In: 2nd smart cities symposium (SCS’19), Bahrain, 24–26 March 2019. New York: IEEE.
13.
YurtseverELambertJCarballoA, et al. A survey of autonomous driving: common practices and emerging technologies, 2020, https://arxiv.org/abs/1906.05113
14.
CheEJungJOlsenMJ.Object recognition, segmentation, and classification of mobile laser scanning point clouds: a state of the art review. Sensors2019; 19: 810.
LidmanPLuuS.Clustering, shape extraction and velocity estimation applied to radar detections. M.sc. Thesis, Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, 2018.
17.
FaragW.Real-time detection of road lane-lines for autonomous driving. Recent Adv Comput Sci Commun2020; 13: 265–274.
18.
FaragW.A comprehensive vehicle-detection-and-tracking technique for autonomous driving. Int J Comput Digital Syst2020; 9(4): 567–580.
19.
DietmayerKKellnerDKlappsteinJ. Grid-based DBSCAN for clustering extended objects in radar data. In: IEEE intelligent vehicles symposium, Alcala De Henares, 3–7 June 2012. New York: IEEE.
20.
FischlerMBollesR.Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Comm ACM1981; 24(6): 381–395.
21.
GohringDWangMSchnurmacherM, et al. Radar/lidar sensor fusion for car-following on highways. In: 5th international conference on automation, robotics and applications, Wellington, 6–8 December 2011, pp.407–412. New York: IEEE.
22.
KaempchenNFuerstenbergKCSkibickiAG, et al. Sensor fusion for multiple automotive active safety and comfort applications. In: ValldorfJGessnerW (eds) Advanced microsystems for automotive applications, vol. 2. New York: Springer, 2004, pp.137–163.
Chavez-GarciaROAycardO.Multiple sensor fusion and classification for moving object detection and tracking. IEEE T Intel Transp Syst2015; 17(2): 252–534.
25.
RangeshATrivediMM.No blind spots: full-surround multi-object tracking for autonomous vehicles using cameras and LiDARs. IEEE T Intel Veh2019; 4: 588–589.
26.
HajriHRahalMC.Real-time lidar and radar high-level fusion for obstacle detection and tracking with evaluation on a ground truth, 2019, https://arxiv.org/abs/1807.11264
27.
JahromiBSTulabandhulaTCetinS.Real-time hybrid multi-sensor fusion framework for perception in autonomous vehicles. Sensors2019; 19(20): 4357.
28.
WanEAVan Der MerweR. The unscented Kalman filter for nonlinear estimation. In: Proceedings of the IEEE 2000 adaptive systems for signal processing, communications, and control symposium (Cat. No.00EX373), Lake Louise, AB, Canada, 4 October 2000. New York: IEEE.
29.
JulierSJUhlmannJK.Unscented filtering and nonlinear estimation. Proc IEEE2004; 92(3): 401–422.
30.
EinickeGAWhiteLB.Robust extended Kalman filtering. IEEE T Signal Pr1999; 47(9): 2596–2599.
31.
BestMCBogdanskiK.Extending the Kalman filter for structured identification of linear and nonlinear systems. Int J Model Ident Control2017; 27(2): 114–124.
32.
SchubertRRichterEWanielikG. Comparison and evaluation of advanced motion models for vehicle tracking. In: 11th international conference on information fusion, Cologne, 30 June–3 July 2008. New York: IEEE.
33.
SanderJXuXEsterM, et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining, Portland, OR, 2–4 August 1996, pp.226–231. New York: IEEE.
NagiubMFaragW. Automatic selection of compiler options using genetic techniques for embedded software design. In: 2013 IEEE 14th international symposium on computational intelligence and informatics (CINTI), Budapest, Hungary, 19–21 November 2013. New York: IEEE.
PichéR.Online tests of Kalman filter consistency. Int J Adapt Control Signal Pr2016; 30(1): 115–124.
39.
ZhaoSHuangB. On initialization of the Kalman filter. In: 2017 6th international symposium on advanced control of industrial processes (AdCONIP), Taipei, Taiwan, 28–31 May 2017. New York: IEEE.
aragW.Synthesis of intelligent hybrid systems for modeling and control. PhD Thesis, University of Waterloo, ON, Canada, 1998.
42.
FaragW.A lightweight vehicle detection and tracking technique for advanced driving assistance systems. J Intel Fuzzy Syst2020; 39(3): 2693–2710.
43.
FaragW.Complex track maneuvering using real-time MPC control for autonomous driving. Int J Comput Digital Syst2020; 9(5): 909–920.
44.
NorouziAMasoumiMBarariA, et al. Lateral control of an autonomous vehicle using integrated backstepping and sliding mode controller. Proc IMechE, Part K: J Multi-body Dynamics2019; 233(1): 141–151.
45.
NorouziAKazemiRAzadiS.Vehicle lateral control in the presence of uncertainty for lane change maneuver using adaptive sliding mode control with fuzzy boundary layer. Proc IMechE, Part I: J Systems and Control Engineering2018; 232(1): 12–28.
46.
HangPChenX.Integrated chassis control algorithm design for path tracking based on four-wheel steering and direct yaw-moment control. Proc Inst Mech Eng, Part I: J Systems and Control Engineering2019; 233(6): 625–641.
