IP Library Granted Patent US 12,325,420
Granted Patent B2
US 12,325,420 · App. 18/078,499 · Granted Jun 10, 2025

Maintaining a range of a gap between an ego vehicle and a preceding vehicle

Inventors: Yashar Zeiynali Farid (Berkeley, CA); Kentaro Oguchi (Mountain View, CA)
Assignees: Toyota Motor Engineering & Manufacturing North America, Inc.; Toyota Jidosha Kabushiki Kaisha
B60W30/17B60W40/10B60W2554/80B60W2556/45
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Quick Facts
Patent No.
US 12,325,420
App. No.
18/078,499
Granted
Jun 10, 2025
Kind
B2
Abstract

A system for maintaining a range of a gap between an ego vehicle and a preceding vehicle can include a processor and a memory. The memory can store a calculations module and an actuation module. The calculations module can cause the processor to determine a time, during a deceleration phase or an acceleration phase of a stop-and-go cycle of the preceding vehicle, when a preceding vehicle speed will equal a desired speed of the ego vehicle. The calculations module can cause the processor to determine: (1) in response to the time being during the deceleration phase, that the gap will be smaller than a desired maximum gap or (2) in response to the time being during the acceleration phase, that the gap will be larger than a desired minimum gap. The actuation module can cause the processor to change an ego vehicle speed to correct the gap.

Claims (85)

1. A system, comprising:

a processor; and

a memory storing:

a calculations module including instructions that, when executed by the processor, cause the processor to:

determine a time, during a deceleration phase or an acceleration phase of a stop-and-go cycle of a preceding vehicle, when a preceding vehicle speed will equal a desired speed of an ego vehicle; and

determine, in response to the time being during:

the deceleration phase, that a gap, between the preceding vehicle and the ego vehicle, will be smaller than a desired maximum gap, or

the acceleration phase, that the gap will be larger than a desired minimum gap; and

an actuation module including instructions that, when executed by the processor, cause the processor to change an ego vehicle speed to optimize a model of a cruise control system to correct the gap by:

adding a product of a gain multiplied by a difference to the model as a gap maintaining term,

adding, in response to a condition in which an absolute value of a difference of the desired speed subtracted from the preceding vehicle speed being less than a threshold, at least one term to the model, or

changing, in the model, a product of a gain multiplied by a first difference to account for a period of the stop-and-go cycle.

2. The system of claim 1 , wherein:

the instructions to change the ego vehicle speed include instructions to cause the cruise control system, of the ego vehicle, to operate to change the ego vehicle speed,

the cruise control system is configured to optimize the model to maintain a range of the gap, and

the instructions to change the ego vehicle speed include at least one of instructions to change the at least one term in the model, instructions to add the at least one term to the model, or instructions to remove the at least one term from the model.

3. The system of claim 2 , wherein the model includes an expression for an ego vehicle acceleration.

4. The system of claim 3 , wherein the instructions to change the ego vehicle speed include instructions to add the at least one term to the expression for the ego vehicle acceleration.

5. The system of claim 4 , wherein:

the at least one term, added to the expression for the ego vehicle acceleration, includes the product of the gain multiplied by the difference, and

in response to the time, when the preceding vehicle speed will equal the desired speed, being during:

the deceleration phase:

the gain is a first gain, and

the difference is the desired maximum gap subtracted from a current gap between the ego vehicle and the preceding vehicle, or

the acceleration phase:

the gain is a second gain, and

the difference is the desired minimum gap subtracted from the current gap.

6. The system of claim 4 , wherein the instructions to add the at least one term to the expression for the ego vehicle acceleration include instructions to add, in response to an existence of the condition, the at least one term to the expression for the ego vehicle acceleration.

7. The system of claim 6 , wherein the condition comprises:

in response to the time, when the preceding vehicle speed will equal the desired speed, being during:

the deceleration phase, a preceding vehicle acceleration being negative, or

the acceleration phase, the preceding vehicle acceleration being positive.

8. The system of claim 3 , wherein the instructions to change the ego vehicle speed include instructions to change the at least one term of the expression for the ego vehicle acceleration.

9. The system of claim 8 , wherein the at least one term of the expression for the ego vehicle acceleration includes the product of the gain multiplied by the first difference, the first difference being the ego vehicle speed subtracted from the desired speed.

10. The system of claim 9 , wherein the instructions to change the at least one term of the expression for the ego vehicle acceleration include instructions to change, in response to an existence of a different condition, the desired speed to a modified desired speed.

11. The system of claim 10 , wherein the different condition comprises a current time being between the time when the preceding vehicle speed will equal the desired speed and a desired time.

12. The system of claim 10 , wherein:

the modified desired speed is a sum of the desired speed added to a ratio,

the ratio is a quotient of a second difference divided by a duration of time,

the duration of time is between the time when the preceding vehicle speed will equal the desired speed and a desired time, and

the second difference is, in response to the time when the preceding vehicle speed will equal the desired speed being during:

the deceleration phase, the desired maximum gap subtracted from a current gap between the ego vehicle and the preceding vehicle, or

the acceleration phase, the desired minimum gap subtracted from the current gap.

13. The system of claim 12 , wherein the duration of time equals the period of the stop-and-go cycle.

14. A method, comprising:

determining, by a processor, a time, during a deceleration phase or an acceleration phase of a stop-and-go cycle of a preceding vehicle, when a preceding vehicle speed will equal a desired speed of an ego vehicle;

determining, by the processor, in response to the time being during:

the deceleration phase, that a gap, between the preceding vehicle and the ego vehicle, will be smaller than a desired maximum gap, or

the acceleration phase, that the gap will be larger than a desired minimum gap; and

changing, by the processor, an ego vehicle speed to optimize a model of a cruise control system to correct the gap by:

adding a product of a gain multiplied by a difference to the, model as a gap maintaining term,

adding, in response to a condition in which an absolute value of a difference of the desired speed subtracted from the preceding vehicle speed being less than a threshold, at least one term to the model, or

changing, in the model, a product of a gain multiplied by a first difference to account for a period of the stop-and-go cycle.

15. The method of claim 14 , further comprising determining, by the processor, the desired maximum gap.

16. The method of claim 15 , wherein the determining the desired maximum gap comprises adding the desired minimum gap to a second difference.

17. The method of claim 16 , wherein:

the time, when the preceding vehicle speed will equal the desired speed, comprises a first time and a second time,

the first time is during the deceleration phase,

the second time is during the acceleration phase,

a first position is a position of the preceding vehicle at the first time,

a second position is a position of the preceding vehicle at the second time,

a third position is a position of the ego vehicle at the first time,

a fourth position is a position of the ego vehicle at the second time,

the first difference is a second difference subtracted from a third distance,

the second difference is the third position subtracted from the fourth position, and

the third difference is the first position subtracted from the second position.

18. The method of claim 17 , wherein:

the second difference is a first term subtracted from a second term,

the first term is the desired speed multiplied by a duration of time, the duration of time being between the first time and the second time, and

a maximum value of the second term is a distance traversed by the preceding vehicle during a single iteration of the stop-and-go cycle.

19. The method of claim 16 , wherein:

the minimum desired gap is a sum of a constant added to a product,

the product is the desired speed multiplied by a duration of time,

the duration of time is between a first time and a second time,

the first time is a time at which the preceding vehicle passes a point, and

the second time is a time at which the ego vehicle, moving at the desired speed, passes the point.

20. A non-transitory computer-readable medium for maintaining a range of a gap between an ego vehicle and a preceding vehicle, the non-transitory computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to:

determine a time, during a deceleration phase or an acceleration phase of a stop-and-go cycle of a preceding vehicle, when a preceding vehicle speed will equal a desired speed of an ego vehicle;

determine, in response to the time being during:

the deceleration phase, that a gap, between the preceding vehicle and the ego vehicle, will be smaller than a desired maximum gap, or

the acceleration phase, that the gap will be larger than a desired minimum gap; and

change an ego vehicle speed to optimize a model of a cruise control system to correct the gap by:

adding a product of a gain multiplied by a difference to the, model as a gap maintaining term,

adding, in response to a condition in which an absolute value of a difference of the desired speed subtracted from the preceding vehicle speed being less than a threshold, at least one term to the model, or

changing, in the model, a product of a gain multiplied by a first difference to account for a period of the stop-and-go cycle.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 11, 2025
From: TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC.
To: TOYOTA JIDOSHA KABUSHIKI KAISHA
Reel/Frame 071670/0085 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 17, 2023
From: FARID, YASHAR ZEIYNALI; OGUCHI, KENTARO
To: TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC.; TOYOTA JIDOSHA KABUSHIKI KAISHA
Reel/Frame 063021/0120 →
Continuity (1)
Related Publication 20240190434A1 · Jun 13, 2024
References Cited (41)
US 20170259822A1 · Schubert · 2017 [cited by applicant]
US 20180194352A1 · Avedisov · 2018 [cited by examiner]
US 20220363279A1 · Kwon · 2022 [cited by examiner]
CN 111016894A · 2020 [cited by applicant]
Stern et al. “Dissipation of stop-and-go waves via control of autonomous vehicle” Transportation Research Part C: Emerging Technologies, vol. 89, 2018, pp. 205-221, ISSN 0968-090X, (Year: 2018). [cited by examiner]
C. Wu, A. M. Bayen and A. Mehta, “Stabilizing Traffic with Autonomous Vehicles,” 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 2018, pp. 6012-6018, doi: 10.1109/ICRA.201… [cited by examiner]
Canale et al. “Personalization of ACC Stop and Go Task Based on Human Driver Behaviour Analysis,” IFAC Proceedings vols. vol. 35, issue 1, 2002, pp. 357-362. [cited by applicant]
Stern et al. “Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments,” Transportation Research Part C: Emerging Technologies, vol. 89, Apr. 2018, pp. 205-221. [cited by applicant]
He et al. “A Jam-Absorption Driving Strategy for Mitigating Traffic Oscillations,” in IEEE Transactions on Intelligent Transportation Systems, vol. 18, No. 4, pp. 802-813. [cited by applicant]
Tadaki et al. “Phase transition in traffic jam experiment on a circuit,” New Journal of Physics, vol. 15, Oct. 2015, pp. 1-20. [cited by applicant]
Schakel et al. “Effects of Cooperative Adaptive Cruise Control on Traffic Flow Stability,” 13th International IEEE Conference on Intelligent Transportation Systems, 2010, pp. 759-764. [cited by applicant]
Kesting et al., “Adaptive cruise control design for active congestion avoidance,” Transportation Research Part C 16, 2008, pp. 668-683. [cited by applicant]
Unknown, “Accordion effect,” last accessed on Aug. 11, 2022, 2 pages, found at https://en.wikipedia.org/wiki/Accordion_effect. [cited by applicant]
Unknown, “Road surface,” last accessed on Nov. 10, 2022, 16 pages, found at https://en.wikipedia.org/wiki/Road_surface. [cited by applicant]
Unknown, “Traffic,” last accessed on Nov. 7, 2022, 15 pages, found at https://en.wikipedia.org/wiki/Traffic. [cited by applicant]
Unknown, “Traffic congestion,” last accessed on Dec. 8, 2022, 29 pages, found at https://en.wikipedia.org/wiki/Traffic_congestion. [cited by applicant]
Unknown, “Traffic wave,” last accessed on Oct. 10, 2022, 2 pages, found at https://en.wikipedia.org/wiki/Traffic_wave. [cited by applicant]
Suh et al., “An Empirical Study on the Traffic State Evolution and Stop-and-Go Traffic Development on Freeways,” Transportmetrica A Transport Science, vol. 12, No. 1, 2016, pp. 80-97. [cited by applicant]
Malikopoulos et al., “Optimal Control for Speed Harmonization of Automated Vehicles,” IEEE Transactions on Intelligent Transportation Systems, 2018, pp. 1-13. [cited by applicant]
Li et al., “Cooperative Perception for Estimating and Predicting Microscopic Traffic States to Manage Connected and Automated Traffic,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, No. 8, pp. 13694-… [cited by applicant]
Yuan et al., “Real-Time Lagrangian Traffic State Estimator for Freeways,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, No. 1, pp. 59-70. [cited by applicant]
Mladen Čičić, “Modelling and Lagrangian Control of Mixed Traffic: Platoon Coordination, Congestion Dissipation and State Reconstruction,” Doctoral Thesis in Electrical Engineering, KTH Royal Institute of Technology, Sto… [cited by applicant]
Suriyarachchi et al., “Shock Wave Mitigation in Multi-Lane Highways Using Vehicle-to-Vehicle Communication,” 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 2021, pp. 1-7. [cited by applicant]
Zheng et al., “Freeway traffic oscillations: Microscopic analysis of formations and propagations using Wavelet Transform,” Procedia Social and Behavioral Sciences 17, 2011, pp. 717-731. [cited by applicant]
Sugiyama et al., “Traffic jams without bottlenecks-experimental evidence for the physical mechanism of the formation of a jam,” New Journal of Physics, vol. 10 033001, 2008, pp. 1-7. [cited by applicant]
William Beaty, “Traffic Experiments and a Cure for Waves & Jams,” last accessed on Jun. 6, 2022, 13 pages, found at http://www.amasci.com/amateur/traffic/trafexp.html. [cited by applicant]
Cui et al., “Stabilizing traffic flow via a single autonomous vehicle: Possibilities and limitations,” 2017 IEEE Intelligent Vehicles Symposium (IV), 2017, pp. 1336-1341. [cited by applicant]
Wu et al., “Stabilizing Traffic with Autonomous Vehicles,” 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 6012-6018. [cited by applicant]
Goulet et al., “Impacts of distributed speed harmonization and optimal maneuver planning on multi-lane roads,” 2020 IEEE Conference on Control Technology and Applications (CCTA), 2020, pp. 305-311. [cited by applicant]
Xie et al., “Cooperative driving strategies of connected vehicles for stabilizing traffic flow,” Transportmetrica B: Transport Dynamics, 2020, V. 8 (1), pp. 166-181. [cited by applicant]
Hale et al., “Introduction of Cooperative Vehicle-to-Infrastructure Systems to Improve Speed Harmonization, Federal Highway Administration,” Mar. 1, 2016, 54 pages. [cited by applicant]
Kates et al., “Flow stabilization and enhanced traffic performance using inter-vehicle communication and distributed Intelligence,” 13th World Congress on Intelligent Transport Systems and Services, 2006, pp. 1-8. [cited by applicant]
Learn et al., “Freeway speed harmonization experiment using connected and automated vehicles, ” IET Intelligent Transport Systems, (2018) V 12 (5), pp. 319-326. [cited by applicant]
U.S. Dept. of Transportation, “Freight Performance Measure Approaches for Bottlenecks, Arterials, and Linking Volumes to Congestion Report,” Aug. 2015, 104 pages, found at https://rosap.ntl.bts.gov/view/dot/41268/dot_41… [cited by applicant]
Čičić et al., “Platoon-Actuated Variable Area Mainstream Traffic Control for Bottleneck Decongestion,” European Journal of Control, vol. 68, Nov. 2022, pp. 1-8. [cited by applicant]
Čičić et al., “Coordinating Vehicle Platoons for Highway Bottleneck Decongestion and Throughput Improvement,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, No. 7, Jul. 2022, pp. 8959-8971. [cited by applicant]
Ibrahim et al., “Control of Platooned Vehicles in Presence of Traffic Shock Waves,” 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, pp. 1727-1734. [cited by applicant]
Chou et al., “The Lord of the Ring Road: A Review and Evaluation of Autonomous Control Policies for Traffic in a Ring Road,” ACM Transactions on Cyber-Physical Systems, vol. 6, No. 1, Jan. 2022, pp. 1-2. [cited by applicant]
Nishi et al., “Theory of jam-absorption driving,” Transportation Research Part B 50, 2013, pp. 116-129. [cited by applicant]
Di Vaio et al., “Cooperative Shock Waves Mitigation in Mixed Traffic Flow Environment,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, No. 12, pp. 4339-4353. [cited by applicant]
Li et al., “Stop-and-go traffic analysis: Theoretical properties, environmental impacts and oscillation mitigation,” Transportation Research Part B 70, 2014, pp. 319-339. [cited by applicant]