US-12626592-B2 - Cloud-based stop-and-go mitigation system with multi-lane sensing

US12626592B2US 12626592 B2US12626592 B2US 12626592B2US-12626592-B2

Abstract

Systems and methods are provided for activating mitigation strategies through a cloud-based system. Embodiments of the systems and methods disclosed herein can provide mitigation strategies to reduce or eliminate the stop-and-go traffic. A control vehicle can activate a mitigation strategy and operate the vehicle in accordance with the mitigation strategy based on stop-and-go waves. The mitigation strategy may comprise maintaining the vehicle at a reference speed.

Inventors

Yashar Zeiynali Farid
Kentaro Oguchi

Assignees

TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC.

Dates

Publication Date: 20260512
Application Date: 20220913

Claims (15)

1 . A method comprising: identifying trajectories of a plurality of vehicles traveling in a same direction on a same road, the plurality of vehicles comprising a control vehicle; based on the trajectories, generating a plurality of stop-and-go waves representing predicted future velocities of traversing vehicles within different positions of a bottleneck at different times, the traversing vehicles comprising at least a subset of the vehicles or different vehicles, wherein at least one stop-and-go wave of the plurality of stop-and-go waves comprises an acceleration region and a deceleration region; determining one or more cyclical characteristics of the at least one stop-and-go wave of the plurality of the stop-and-go waves, wherein the one or more cyclical characteristics comprise a period or a wavelength; defining a control zone based on the bottleneck and based on the one or more cyclical characteristics of the at least one stop-and-go wave; and operating the control vehicle by programming one or more operating characteristics of the control vehicle at the different times based on the one or more cyclical characteristics, a current position of the control vehicle, and the predicted future velocities, wherein programming one or more operating characteristics comprises: in response to determining that the bottleneck comprises a fixed waiting time attribute: applying a prediction model to predict a waiting time duration based on historical trajectory data or historical infrastructure data; and regulating one or more velocities of the control vehicle based on the predicted waiting time duration.
2 . The method of claim 1 , wherein the determining of the one or more cyclical characteristics comprises determining the at least one stop-and-go wave of the plurality of stop-and-go waves has a highest wavelength relative to other stop-and-go waves of the plurality of stop-and-go waves; and the method further comprises: setting an entrance boundary for the control zone based on the highest wavelength.
3 . The method of claim 1 , wherein the programming of the one or more operating characteristics comprises: in response to determining that the bottleneck comprises a fixed cycle time duration attribute: selecting a stop-and-go wave of the plurality of stop-and-go waves; and regulating the one or more velocities of the control vehicle based on the fixed cycle time duration and the selected stop-and-go wave.
4 . The method of claim 1 , wherein the programming of the one or more operating characteristics comprises setting an initial velocity and updating the initial velocity after a stop-and-go wave.
5 . The method of claim 1 , wherein the programming of the one or more operating characteristics is based on historical stop-and-go waves corresponding to previous occurrences of the bottleneck.
6 . The method of claim 1 , wherein each stop-and-go wave of the plurality of stop-and-go waves comprises a deceleration region, a stopping region and a cruising region.
7 . A cloud-based system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to: identify trajectories of a plurality of vehicles traveling in a same direction on a same road, the plurality of vehicles comprising a control vehicle; based on the trajectories, generate a plurality of stop-and-go waves representing predicted future velocities of traversing vehicles within different positions of a bottleneck at different times, the traversing vehicles comprising at least a subset of the vehicles or different vehicles, wherein at least one stop-and-go wave of the plurality of stop-and-go waves comprises an acceleration region and a deceleration region; determining one or more cyclical characteristics of the at least one stop-and-go wave of the plurality of the stop-and-go waves, wherein the one or more cyclical characteristics comprise a period or a wavelength; define a control zone based on the bottleneck and based on the one or more cyclical characteristics of the at least one stop-and-go wave; and operate the control vehicle by programming one or more operating characteristics of the control vehicle at the different times based on the one or more cyclical characteristics, a current position of the control vehicle, and the predicted future velocities, wherein programming one or more operating characteristics comprises: in response to determining that the bottleneck comprises a fixed waiting time attribute: applying a prediction model to predict a waiting time duration based on historical trajectory data or historical infrastructure data; and regulating one or more velocities of the control vehicle based on the predicted waiting time duration.
8 . The cloud-based system of claim 7 , wherein the programming of the one or more operating characteristics comprises: in response to determining that the bottleneck comprises a fixed cycle time duration attribute: selecting a stop-and-go wave of the plurality of stop-and-go waves; and regulating the one or more velocities of the control vehicle based on the fixed cycle time duration and the selected stop-and-go wave.
9 . The cloud-based system of claim 7 , wherein the programming of the one or more operating characteristics comprises setting an initial velocity and updating the initial velocity after a stop-and-go wave.
10 . The cloud-based system of claim 7 , wherein the programming of the one or more operating characteristics is based on historical stop-and-go waves corresponding to previous occurrences of the bottleneck.
11 . The cloud-based system of claim 7 , wherein the programming of the one or more operating characteristics is based on an average speed of the plurality of vehicles.
12 . The method of claim 2 , wherein the setting of the entrance boundary comprises determining a position of a rear bumper of a last vehicle of the plurality of vehicles within the bottleneck, and setting the entrance boundary as an offset by the highest wavelength from the position of the rear bumper of the last vehicle to thereby provide a buffering distance to program the one or more operating characteristics before encountering a stop-and-go wave.
13 . The method of claim 1 , wherein the at least one stop-and-go wave comprises a first stop-and-go wave having a first deceleration region, a first stopping region, and a first acceleration region and a second stop-and-go wave adjacent to the first stop-and-go wave, the second stop-and-go wave comprising a second deceleration region immediately adjacent to the first acceleration region of the first stop-and-go wave.
14 . The method of claim 13 , wherein the one or more operating characteristics comprises a velocity, and the first stop-and-go wave has a first wavelength different from a second wavelength of the second stop-and-go wave.
15 . The method of claim 1 , wherein the programming of the one or more operating characteristics is based on a jerk of the control vehicle, a traffic oscillation caused by the programming of the one or more operating characteristics, and predicted braking forces corresponding to the control vehicle and one or more other vehicles within a threshold distance of the control vehicle resulting from the programming of the one or more operating characteristics.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is co-pending with U.S. patent application Ser. No. 17/943,803 and U.S. patent application Ser. No. 17/943,917, both of which were filed concurrently with the present application. Each of these applications are hereby incorporated herein by reference in their entirety. TECHNICAL FIELD The present disclosure relates generally to mitigating stop-and-go traffic, and in particular, some implementations may relate to determining stop-and-go waves and activating mitigating strategies based on the waves. DESCRIPTION OF RELATED ART Stop-and-go traffic refers to the phenomenon where vehicles in traffic experience periods of deceleration. Stop-and-go traffic can occur for various reasons, including metered lights, lane changes, accidents, or other obstacles encountered during traffic. Mitigation strategies can be applied to reduce stop-and-go traffic or prevent such a traffic situation from occurring. However, it can be difficult for drivers to apply these strategies because the driver might not be able to perceive a stop-and-go situation until the vehicle is forced to slow down. Better methods are needed to improve automated vehicle operation and transit strategies overall. BRIEF DESCRIPTION OF THE DRAWINGS The present disclosure, in accordance with one or more various embodiments, is described in detail with reference to the following figures. The figures are provided for purposes of illustration only and merely depict typical or example embodiments. FIG. 1 is a schematic representation of an example hybrid vehicle with which embodiments of the systems and methods disclosed herein may be implemented. FIG. 2 illustrates an example of an all-wheel drive hybrid vehicle with which embodiments of the systems and methods disclosed herein may be implemented. FIG. 3A illustrates an example cloud system in accordance with various embodiments. FIG. 3B illustrates an example reinforced learning control module in accordance with various embodiments. FIG. 4 illustrates an example measurement of stop-and-go waves in accordance with embodiments of the systems and methods disclosed herein. FIG. 5A illustrates an example cloud-computing system in accordance with various embodiments described herein. FIG. 5B illustrates an example system for Type 1 and Type 2 bottlenecks in accordance with various embodiments. FIG. 6 illustrates an example method in accordance with various embodiments. FIG. 7 illustrates an example system for determining deceleration profiles in accordance with various embodiments. FIG. 8 illustrates an example method in accordance with various embodiments. FIG. 9 is an example computing component that may be used to implement various features of embodiments described in the present disclosure. The figures are not exhaustive and do not limit the present disclosure to the precise form disclosed. DETAILED DESCRIPTION As described above, stop-and-go traffic can occur for various reasons, including metered lights, lane changes, accidents, or other obstacles encountered during traffic. Embodiments of the systems and methods disclosed herein can provide mitigation strategies to reduce or eliminate the stop-and-go traffic. Mitigation strategies can be applied to reduce stop-and-go traffic or prevent such a traffic situation from occurring. For example, a cloud-based system can predict when to apply mitigation strategies based on local vehicle data (e.g., from a control vehicle), remote vehicle data (e.g., data from connected vehicles within a proximity distance of the control vehicle that are shared via a cloud or directly from the vehicle), and infrastructure data. As used herein, “stop-and-go wave” can refer to an event where a vehicle is caused to slow down and stop due to a traffic element, and then accelerates back to a cruising speed after a time period. A “phase” can refer to a partition of a stop-and-go wave characterized by a particular trend in the vehicle's velocity (deceleration, acceleration, etc.). A vehicle's “trajectory” can refer to a vehicle's position over time, including its predicted position at a future time. A “control zone” can refer to a bounded area where a vehicle can apply mitigation strategies to reduce or eliminate stop-and-go waves. A “deceleration profile” can refer to a vehicle's predicted deceleration due to a stop-and-go wave. A “wavelength” of a stop-and-go wave can refer to the distance where a vehicle decelerates due to traffic, stops, accelerates to a cruising speed, and then is forced to decelerate again. Stop-and-go waves can be determined based on a trajectory. Cloud-based systems can use these stop-and-go waves to suggest mitigation strategies to vehicles before the vehicles experience stop-and-go waves. For example, a stop-and-go wave can comprise a deceleration phase, a stopping phase, an acceleration phase, and a cruising phase. Each wavelength may have a time threshold that dictates the length of the wavelength. The time