1 of 14

Motorcycle Lateral Position and Control

By Bryson Kronheim

2 of 14

Background Information

  • Minibike details
    • Developed during summer research
    • Self stabilizing
    • Bike achieves goal roll angles
  • Current Issues
    • Deviations from straight line paths
    • Increased difficulty for testing

3 of 14

Thesis Motivation

  • Initial proposed controller to remedy these issues
    • Ensures straight line driving behavior
    • Allows for lane change maneuvers
    • Works in simulation
  • Real world implementation unsatisfactory
    • Error in odometry estimates of lane position
    • Unstable performance

4 of 14

Literature Review

  • Current research shows several methods to better predict lateral position

​

  1. Camera based localization
  2. Sensor fusion algorithms utilizing GPS and additional sensors
  3. Sensor fusion algorithms using only relative positioning sensors

5 of 14

Literature Review: Camera based approach

    • Inverse Perspective Mapping
    • Spatial coordinates of pixels are determined
    • Curve fitting of the lanes to determine lateral position

Camera based lateral positioning

    • Cameras aren’t fully reliable
    • Gaps in data collection

Drawbacks

Fig. 1 [2], Image-Based Lateral Position, Steering Behavior Estimation, and Road Curvature Prediction for Motorcycles

6 of 14

Literature Review: GPS usage

  • TS
    • Georeferenced total station
  • AUKF
    • Augmented unscented Kalman filter
  • GNSS
    • Global navigation satellite system

​

  • Mean 2D position error of 0.79 m with a standard deviation of 0.51 m.

Fig. 2 [6], Mobile App Driven Localization for Motorcycles Based on an Adaptive Unscented Kalman Filter

7 of 14

Literature Review: Relative position sensors

  • Tested in BikeSim
  • Uses interconnected lateral and longitudinal models
    • Takagi-Sugeno Form Observer
  • Measured values
    • Steering angle
    • Roll rate
    • Yaw rate
    • Longitudinal/Lateral accelerations
    • Wheel speeds

​

Fig. 3 [12], Interconnected Observers for a Powered Two-Wheeled Vehicles: Both Lateral and Longitudinal Dynamics Estimation

8 of 14

Methodology (Simulation Phase)

9 of 14

Methodology (Prototype Phase)

10 of 14

Timeline

Month

Goals

October

  • Add the camera to the Webots simulation
  • Determine if a gimbal is necessary
  • Implement the IPM for the camera
  • Use camera specifications to determine lateral position information from the remapped camera information

November/December

  • Determine initial error estimations for sensors
  • Implement sensor fusion algorithm combining lateral position camera estimations and odometry estimations
  • Add GPS to the sensor fusion algorithm
  • Run bike simulation experiments such as the double lane change scenario and collect data

Winter Break (December-January)

January/February

  • Mount the camera on the bike
  • Validate camera lateral predictions through use of robotics lab camera localization
  • Implement sensor fusion algorithm into ROS2

March

  • Finish ROS2 implementation
  • Data collection for the bike when the weather is suitable
    • This will involve the constant lateral position test, as well as the double lane change scenario. Any scenario run will use the predicted lateral positions input into a lane controller to control movement of the bike.

April

  • Any final data collection if necessary
  • Finish up thesis paper writing

May

  • Revise thesis paper
  • Thesis presentation preparation

11 of 14

Budget

Category

Item

Unit Cost

Quantity

Total

Camera System

Jetson Nano

$150

1

$150

1080p Camera

$100

1

$100

Gimbal

$100

1

$100

General Materials

Lane Marking Paint

$50

1

$50

3D Printing Filament

N/A

N/A

$25

�

Total Cost

$425

12 of 14

Conclusion

Main issue

    • Odometry based lane keeping is unsatisfactory

Solution

    • Camera based lateral positioning
    • GPS localization
    • Sensor fusion algorithm
    • Implementation on real world minibike

13 of 14

References

  • [1] O. Alrazouk, A. Chellali, L. Nehaoua and H. Arioui, "Vision-based approach for estimating lateral dynamics of Powered Two-Wheeled Vehicles," 2023 American Control Conference (ACC), San Diego, CA, USA, 2023, pp. 999-1005, doi: 10.23919/ACC55779.2023.10155807.
  • [2] P. -M. Damon, H. Hadj-Abdelkader, H. Arioui and K. Youcef-Toumi, "Image-Based Lateral Position, Steering Behavior Estimation, and Road Curvature Prediction for Motorcycles," in IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 2694-2701, July 2018, doi: 10.1109/LRA.2018.2831260.
  • [3] O. Alrazouk, A. Chellali, H. Hadj-Abdelkader and H. Arioui, "Vision-Based Estimation of Motorcycle Attitude," in IEEE Robotics and Automation Letters, vol. 8, no. 9, pp. 5759-5766, Sept. 2023, doi: 10.1109/LRA.2023.3300543.
  • [4] A. Serov, J. Clemens and K. Schill, "Visual-Inertial Odometry aided by Speed and Steering Angle Measurements," 2022 25th International Conference on Information Fusion (FUSION), Linköping, Sweden, 2022, pp. 1-8, doi: 10.23919/FUSION49751.2022.9841243.
  • [5] G. Park, "Interacting Multiple Model Kalman Filtering for Optimal Vehicle State Estimation," 2022 2nd International Conference on Robotics, Automation and Artificial Intelligence (RAAI), Singapore, Singapore, 2022, pp. 87-91, doi: 10.1109/RAAI56146.2022.10093004.
  • [6] C. Hebisch, D. Benz, A. Lemmer and D. Abel, "Mobile App Driven Localization for Motorcycles Based on an Adaptive Unscented Kalman Filter," 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, 2022, pp. 3831-3836, doi: 10.1109/ITSC55140.2022.9922185.
  • [7] S. Rezaei and R. Sengupta, "Kalman Filter-Based Integration of DGPS and Vehicle Sensors for Localization," in IEEE Transactions on Control Systems Technology, vol. 15, no. 6, pp. 1080-1088, Nov. 2007, doi: 10.1109/TCST.2006.886439.
  • [8] M. Bruschetta, L. Caiaffa, E. Picotti and A. Beghi, "Velocity Aided, Correlated Noise Extended Kalman Filtering for Attitude Estimation: a Motorcycle Case Study," 2021 29th Mediterranean Conference on Control and Automation (MED), PUGLIA, Italy, 2021, pp. 305-310, doi: 10.1109/MED51440.2021.9480325.
  • [9] Y. Wang, J. Mangnus, D. Kostić, H. Nijmeijer and S. T. H. Jansen, "Vehicle state estimation using GPS/IMU integration," SENSORS, 2011 IEEE, Limerick, Ireland, 2011, pp. 1815-1818, doi: 10.1109/ICSENS.2011.6127142.

​

14 of 14

References

  • [10] P. -M. Damon, H. Dabladji, D. Ichalal, L. Nehaoua and H. Arioui, "Estimation of lateral motorcycle dynamics and rider action with Luenberger observer," 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, 2016, pp. 2392-2397, doi: 10.1109/ITSC.2016.7795941.
  • [11] P. -M. Damon, D. Ichalal and H. Arioui, "Steering and Lateral Motorcycle Dynamics Estimation: Validation of Luenberger LPV Observer Approach," in IEEE Transactions on Intelligent Vehicles, vol. 4, no. 2, pp. 277-286, June 2019, doi: 10.1109/TIV.2019.2904384.
  • [12] M. Fouka, L. Nehaoua, H. Arioui and S. Mammar, "Interconnected Observers for a Powered Two-Wheeled Vehicles: Both Lateral and Longitudinal Dynamics Estimation," 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC), Banff, AB, Canada, 2019, pp. 163-168, doi: 10.1109/ICNSC.2019.8743290.
  • [13] M. Fouka, L. Nehaoua and H. Arioui, "Data Calibration and Quasi-LPV Unknown Input Observer: Powered Two-Wheeled Vehicle," 2020 59th IEEE Conference on Decision and Control (CDC), Jeju, Korea (South), 2020, pp. 3921-3926, doi: 10.1109/CDC42340.2020.9304215.
  • [14] H. Slimi, H. Arioui and S. Mammar, "Motorcycle lateral dynamic estimation and lateral tire-road forces reconstruction using sliding mode observer," 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, Netherlands, 2013, pp. 584-589, doi: 10.1109/ITSC.2013.6728294.
  • [15] N. Okamoto and T. Takei, "Environmental mapping by a riding bicycle for a navigation map of autonomous mobile robot," 2015 IEEE/SICE International Symposium on System Integration (SII), Nagoya, Japan, 2015, pp. 369-374, doi: 10.1109/SII.2015.7404948.
  • [16] M. Hrgetić and J. Deur, "Experimental Analysis of Kalman Filter-Based Vehicle Sideslip Angle Estimation Accuracy and Related Error-Compensation Techniques," 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Dubrovnik, Croatia, 2020, pp. 1-6, doi: 10.1109/I2MTC43012.2020.9128588.
  • [17] S. Miah, E. Milonidis, I. Kaparias and N. Karcanias, "An Innovative Multi-Sensor Fusion Algorithm to Enhance Positioning Accuracy of an Instrumented Bicycle," in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1145-1153, March 2020, doi: 10.1109/TITS.2019.2902797.
  • [18] F. Li, "Research on self-balancing unmanned bicycle based on cascade PID control," 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China, 2021, pp. 442-446, doi: 10.1109/ICIBA52610.2021.9687999.
  • [19] Quigley, Morgan, et al. "ROS: an open-source Robot Operating System." ICRA workshop on open source software. Vol. 3. No. 3.2. 2009.