Autonomous vehicle with systems for overtake associated predictive slowdown events

Abstract

An autonomous vehicle includes a system having one or more input devices to generate input signals associated with a distant vehicle. The system also includes a computer having one or more processors programmed to receive the input signals from the input devices. The processors are further programmed to determine a speed profile of the distant vehicle based on the input signals, determine that the distant vehicle is a traffic obstruction based on the input signals, and determine a predicted deceleration event associated with the traffic obstruction and the speed profile. The processors are further programmed to generate an actuation signal in response to the processors determining the speed profile, the traffic obstruction, and the predicted deceleration event. A notification device provides a notification of the predicted deceleration event to a user in response to the notification device receiving the actuation signal from the processors.

Description

Technical Field

[0001] This disclosure relates to systems for monitoring traffic congestion and controlling vehicles based on traffic congestion, and more specifically to autonomous vehicles having systems for determining predicted deceleration events and enabling autonomous vehicles to overtake distant vehicles associated with the predicted deceleration events. Background Technology

[0002] Adaptive cruise control (ACC) systems keep an autonomous (EGO) vehicle at a predetermined distance behind a distant vehicle and within associated speed limits. The ACC system automatically adjusts the autonomous vehicle's speed based on one or more input signals, such as an input signal associated with the real-time speed of a distant vehicle directly upstream of the autonomous vehicle. However, when the distant vehicle obstructs traffic (e.g., the distant vehicle is traveling below a predetermined speed threshold limit, accelerating below a predetermined acceleration threshold, making a predetermined number of stops, etc.), the ACC system may be unable to operate the autonomous vehicle to overtake the distant vehicle. The distant vehicle may obstruct traffic, increasing the autonomous vehicle's travel time and delaying its arrival at its final destination. Therefore, the increased travel time of the autonomous vehicle increases the consumption of vehicle resources (e.g., fuel, battery resources, processing resources, and / or storage resources) and / or network resources (e.g., time-domain resources and / or frequency-domain resources) used to operate the autonomous vehicle.

[0003] Therefore, although the existing ACC system has achieved its intended purpose, a new and improved system is still needed to address these issues. Summary of the Invention

[0004] According to several aspects of this disclosure, an autonomous vehicle includes a longitudinal control submodule for controlling propulsion actuators and / or braking actuators. The autonomous vehicle also includes a lateral control submodule for controlling steering actuators. The autonomous vehicle further includes a system having one or more input devices for generating input signals associated with a remote vehicle. The system also includes one or more notification devices for providing a user with notification that the remote vehicle is a traffic obstacle associated with a predicted deceleration event. The system also includes a computer having one or more processors electronically connected to the longitudinal control submodule, the lateral control submodule, the input devices, and the notification devices. The computer also includes a non-transitory computer-readable storage medium (CRM) storing instructions that program the processor to receive input signals from the input devices. The processor is also programmed to determine, based on the input signals, that the remote vehicle is a traffic obstacle. The processor is also programmed to determine a speed profile of the remote vehicle based on the input signals. The processor is also programmed to determine a predicted deceleration event associated with the traffic obstacle and the speed profile based on the input signals. The processor is also programmed to generate an actuation signal in response to the processor determining the speed profile, the traffic obstacle, and the predicted deceleration event. The notification device provides a notification to the user in response to receiving an actuation signal from the processor, indicating a predicted deceleration event.

[0005] In one aspect, the longitudinal control submodule controls propulsion actuators and / or braking actuators in response to receiving an actuation signal from the processor, enabling the autonomous vehicle to overtake a distant vehicle. The lateral control submodule controls steering actuators in response to receiving an actuation signal from the processor, enabling the autonomous vehicle to overtake a distant vehicle.

[0006] On the other hand, the processor is also programmed to determine the category of the remote vehicle in response to receiving an input signal from the input device. The processor is also programmed to compare the determined category with a lookup table, which includes multiple predetermined categories and multiple associated empirical speed curves, in response to identifying the category of the remote vehicle in the lookup table. The processor is further programmed to determine the speed curve of the remote vehicle in response to identifying the category of the remote vehicle in the lookup table.

[0007] In another aspect, the processor is also programmed to determine one or more surrounding vehicles in response to receiving an input signal from an input device, wherein the distant vehicle and the surrounding vehicles are located in a common area and have a common brand, common model, common model year, common engine type, and / or common engine size. The processor is also programmed to determine the surrounding speed profiles of the surrounding vehicles. The processor is further programmed to define the speed profile of the distant vehicle as the surrounding speed profile of the surrounding vehicles.

[0008] On the other hand, the processor is also programmed to determine a predicted deceleration event in response to the processor determining that the distant vehicle is a traffic obstacle and also based on input signals associated with: an intersection downstream of the distant vehicle; a highway entrance ramp downstream of the distant vehicle; a highway exit ramp downstream of the distant vehicle; a traffic control device downstream of the distant vehicle; a stop sign downstream of the distant vehicle; a bus stop downstream of the distant vehicle; a mailbox downstream of the distant vehicle; a business delivery entrance downstream of the distant vehicle; a train crossing downstream of the distant vehicle; and / or a roadside garbage collection point downstream of the distant vehicle.

[0009] On the other hand, the processor is also programmed to determine, in response to receiving an input signal, that the autonomous vehicle needs to navigate to a target lane to overtake a distant vehicle. The processor is also programmed to determine an obstacle level associated with the target lane in response to receiving an input signal from an input device. The processor is also programmed to compare the obstacle level with a predetermined obstacle threshold. The processor is also programmed to determine that the target lane is unobstructed in response to determining that the obstacle level is below the predetermined obstacle threshold. The processor is also programmed to generate an actuation signal in response to determining that the target lane is unobstructed. The processor is also programmed to determine that an obstacle exists in the target lane in response to determining that the obstacle level is not lower than the predetermined obstacle threshold. The processor is also programmed to avoid generating an actuation signal in response to determining that an obstacle exists in the target lane.

[0010] On the other hand, the processor is also programmed to determine the upcoming turn of the autonomous vehicle and the associated approach distance between the upcoming turn and the current position of a distant vehicle. The processor is also programmed to compare the associated approach distance with a approach distance threshold. The processor is also programmed to generate an actuation signal in response to determining that the associated approach distance is higher than the approach distance threshold. The processor is also programmed to avoid generating an actuation signal in response to determining that the associated approach distance is not higher than the approach distance threshold.

[0011] On the other hand, the speed curve indicates the average acceleration of distant vehicles, and the processor is also programmed to compare the average acceleration of distant vehicles with an acceleration threshold. The processor is also programmed to determine that a distant vehicle is a traffic obstacle in response to the processor determining that the average acceleration is below the acceleration threshold.

[0012] On the other hand, the speed curve indicates the estimated weight of the distant vehicle, and the processor is also programmed to compare the estimated weight of the distant vehicle with a predetermined weight threshold. The processor is also programmed to determine that the distant vehicle is a traffic obstacle in response to the processor determining that the estimated weight is higher than the predetermined weight threshold.

[0013] On the other hand, the speed curve indicates the average speed of distant vehicles, and the processor is also programmed to compare the average speed of distant vehicles with a predetermined average speed threshold. The processor is also programmed to determine that a distant vehicle is a traffic obstacle in response to the processor determining that the average speed is below the predetermined average speed threshold.

[0014] On the other hand, the speed curve indicates the maximum speed of the distant vehicle, and the processor is also programmed to compare the maximum speed of the distant vehicle with a speed threshold. The processor is also programmed to determine that the distant vehicle is a traffic obstacle in response to the processor determining that the maximum speed is below the speed threshold.

[0015] According to several aspects of this disclosure, a computer for a system of autonomous vehicles includes one or more processors electronically connected to a longitudinal control submodule, a lateral control submodule, one or more input devices, and one or more notification devices. The computer also includes a non-transitory computer-readable storage medium (CRM) storing instructions that program the processor to receive input signals from the input devices, wherein the input signals are associated with a remote vehicle. The processor is further programmed to determine, based on the input signals, that the remote vehicle is a traffic obstacle. The processor is further programmed to determine a speed profile of the remote vehicle based on the input signals. The processor is further programmed to determine a predicted deceleration event associated with the traffic obstacle and the speed profile based on the input signals. The processor is further programmed to generate an actuation signal in response to the processor determining the speed profile, the traffic obstacle, and the predicted deceleration event. A notification device, in response to receiving the actuation signal from the processor, provides a user with notification that the remote vehicle is a traffic obstacle associated with the predicted deceleration event.

[0016] In one aspect, the longitudinal control submodule controls propulsion actuators and / or braking actuators in response to receiving an actuation signal from the processor, enabling the autonomous vehicle to overtake a distant vehicle. The lateral control submodule controls steering actuators in response to receiving an actuation signal from the processor, enabling the autonomous vehicle to overtake a distant vehicle.

[0017] On the other hand, the processor is also programmed to determine the category of a distant vehicle in response to receiving an input signal from an input device. The processor is also programmed to compare the category of the distant vehicle with a lookup table, which includes multiple predetermined categories and multiple associated empirical speed curves, in response to determining the category of the distant vehicle. The processor is also programmed to determine the speed curve of the distant vehicle in response to identifying the category of the distant vehicle in the lookup table. The processor is also programmed to determine one or more surrounding vehicles in response to receiving an input signal from an input device, wherein the distant vehicle and surrounding vehicles are located in a common area and have a common brand, common model, common model year, common engine type, and / or common engine size. The processor is also programmed to determine the surrounding speed curves of the surrounding vehicles and to define the speed curve of the distant vehicle as the surrounding speed curve.

[0018] On the other hand, the processor is also programmed to determine that the autonomous vehicle needs to navigate to a target lane that allows it to overtake a distant vehicle. The processor is also programmed to determine an obstacle level associated with the target lane in response to receiving an input signal from an input device. The processor is also programmed to compare the obstacle level with a predetermined obstacle threshold. The processor is also programmed to determine that the target lane is unobstructed in response to determining that the obstacle level is below the predetermined obstacle threshold. The processor is also programmed to generate an actuation signal in response to determining that the target lane is unobstructed. The processor is also programmed to determine that an obstacle exists in the target lane in response to determining that the obstacle level is not below the predetermined obstacle threshold. The processor is also programmed to avoid generating an actuation signal in response to determining that an obstacle exists in the target lane.

[0019] On the other hand, the processor is also programmed to determine the upcoming turn of the autonomous vehicle and the associated approach distance between the upcoming turn and the current position of a distant vehicle. The processor is also programmed to compare the associated approach distance with a approach distance threshold. The processor is also programmed to generate an actuation signal in response to determining that the associated approach distance is higher than the approach distance threshold. The processor is also programmed to avoid generating an actuation signal in response to determining that the associated approach distance is not higher than the approach distance threshold.

[0020] According to several aspects of this disclosure, a method of operating an autonomous vehicle includes generating an input signal associated with a remote vehicle via one or more input devices. The method further includes receiving the input signal from at least one input device via a processor of a computer. The method further includes determining, based on the input signal, that the remote vehicle is a traffic obstacle via the processor. The method further includes determining, based on the input signal, a speed profile of the remote vehicle via the processor. The method further includes determining, based on the input signal, a predicted deceleration event via the processor. The method further includes generating an actuation signal in response to the processor determining the speed profile, the traffic obstacle, and the predicted deceleration event. The method further includes providing a notification to a user via a notification device that the remote vehicle is a traffic obstacle associated with the predicted deceleration event in response to receiving the actuation signal from the processor.

[0021] In one aspect, the method further includes controlling a propulsion actuator and / or a braking actuator via the longitudinal control submodule in response to receiving an actuation signal from the processor, so as to enable the autonomous vehicle to overtake a distant vehicle. The method also includes controlling a steering actuator via the lateral control submodule in response to receiving an actuation signal from the processor, so as to enable the autonomous vehicle to overtake a distant vehicle.

[0022] In another aspect, the method further includes determining the category of a distant vehicle by the processor in response to receiving an input signal from an input device. The method also includes comparing the category of the distant vehicle with a lookup table having multiple predetermined categories and multiple associated empirical speed curves by the processor in response to the processor determining the category of the distant vehicle. The method further includes determining the speed curve of the distant vehicle by the processor in response to the processor identifying the category of the distant vehicle in the lookup table. The method also includes determining one or more surrounding vehicles by the processor in response to receiving an input signal from an input device, wherein the distant vehicle and the surrounding vehicles are located in a common area and have at least one of the following: common brand, common model, common model year, common engine type, and / or common engine size. The method further includes determining the surrounding speed curves of the surrounding vehicles by the processor. The method also includes defining the speed curve of the distant vehicle as the surrounding speed curve of the surrounding vehicles by the processor.

[0023] Further applicability will become apparent from the description provided herein. It should be understood that the descriptions and specific examples are for illustrative purposes only and are not intended to limit the scope of this disclosure. Attached Figure Description

[0024] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this disclosure in any way.

[0025] Figure 1This is a schematic diagram of an example of an autonomous vehicle having a system including a computer for determining a predicted deceleration event associated with a distant vehicle, wherein the system also includes a longitudinal control module and a lateral control module for enabling the autonomous vehicle to overtake the distant vehicle.

[0026] Figure 2 yes Figure 1 The diagram illustrates how the autonomous vehicle overtakes the distant vehicle in response to the system determining that the approach distance between the upcoming turn and the distant vehicle does not exceed a proximity threshold.

[0027] Figure 3 yes Figure 1 The diagram illustrates the autonomous vehicle system determining the speed curve of a distant vehicle based on the known speed curves of surrounding vehicles. The distant vehicle and the surrounding vehicles are located in a common area and share the same brand, model year, engine type, and / or engine size.

[0028] Figure 4 It is an operation Figure 1 A flowchart of an example of an autonomous vehicle approach. Detailed Implementation

[0029] The following description is exemplary in nature and is not intended to limit this disclosure, application, or use.

[0030] This disclosure describes an autonomous vehicle 100 having system 102 (system). Figure 1 In one example, the system has a computer 104 that determines a predicted deceleration event associated with a distant vehicle 106 located upstream of the autonomous vehicle 100, such that the autonomous vehicle 100 can overtake the distant vehicle 106. Figure 2 Non-limiting examples of autonomous vehicle 100 and / or remote vehicle 106 (e.g., passenger car 108, light truck, heavy truck, sport utility vehicle, van, motorhome, bus, commercial truck 110, waste collection vehicle, multi-purpose vehicle, delivery vehicle, emergency vehicle, etc.) may include autonomous vehicles, semi-autonomous vehicles, or non-autonomous (manual) vehicles with associated propulsion systems (e.g., internal combustion engine and / or electric motor, hydrogen fuel cell, etc.). Because autonomous vehicle 100 exceeds remote vehicle 106, autonomous vehicle 100 can reduce travel time, which in turn can reduce the consumption of vehicle resources (e.g., fuel resources, battery resources, processing resources, and / or storage resources) and / or network resources (e.g., time domain resources and / or frequency domain resources) used to operate the autonomous vehicle.

[0031] Reference Figure 1A non-limiting example of the autonomous vehicle 100 is an automated vehicle that includes a longitudinal control submodule 112 for controlling a propulsion actuator 114 and / or a braking actuator 116. The autonomous vehicle 100 may also include a lateral control submodule 118 for controlling a steering actuator 120 of the autonomous vehicle 100. In other non-limiting examples, the autonomous vehicle is a semi-automatic or non-automatic vehicle with an accelerator, brakes, and steering wheel manually operated by a driver.

[0032] The autonomous vehicle 100 also includes a system 102 having one or more input devices 122. Each input device 122 is configured to generate an input signal associated with the remote vehicle 106. More specifically, the input signal may indicate data associated with the autonomous vehicle 100 and / or the remote vehicle 106 (e.g., absolute real-time speed, absolute real-time heading, relative real-time speed, relative real-time heading, average acceleration, estimated weight, average speed, maximum speed, etc.). The input signal may also indicate data associated with road geometry and vulnerable road users (VRUs) (e.g., pedestrians; road workers; people operating wheelchairs or other personal mobility devices (whether motorized or not); people operating electric scooters or similar devices; and people operating bicycles or other non-motorized vehicles).

[0033] Non-limiting examples of input device 122 may include multiple onboard devices (e.g., radar device 124, lidar device 126, externally facing camera 128, night vision camera 130, infrared camera 132, ultrasonic device 134, steering wheel angle sensor 136, brake sensor 138, wheel speed sensor 140, accelerometer 142, and / or automated driving system 144 (ADS), etc.) installed on the autonomous vehicle 100 for generating input signals associated with the movement of the autonomous vehicle 100, remote vehicle 106, and / or VRU. Non-limiting examples of input device 122 may also include non-onboard devices in data and communication networks 146 (e.g., cellular network 148, dedicated short-range communication (DSRC) network 150, vehicle-to-infrastructure (V2X) network 152, global positioning satellite (GPS) network 154, Wi-Fi network 156, road traffic monitoring network 158, road database 160, Internet network 162, etc.). However, it is conceivable that the input device may include other suitable driver monitoring devices, in-vehicle devices, or non-in-vehicle devices. It is also conceivable that the input signal may indicate other suitable parameters to users (e.g., vehicle occupants such as drivers and / or passengers, operators remote from the vehicle, etc.), autonomous vehicles, remote vehicles, road geometry, and VRUs.

[0034] The input device 122 may also include a road detection module 164 for generating road signals. In this non-limiting example, the road detection module 164 may include a radar device 124, a lidar device 126, an externally facing camera 128, a night vision camera 130, an infrared camera 132, an ultrasonic device 134, a cellular network 148, a DSRC network 150, a V2X network 152, a GPS network 154, a Wi-Fi network 156, a road traffic monitoring network 158, a road database 160, and / or an Internet network 162, etc.

[0035] Road signals can indicate road geometry in the following form: intersection 166 located downstream of distant vehicle 106 ( Figure 2 ); highway entrance ramps located downstream of long-distance vehicle 106; highway exit ramps located downstream of long-distance vehicle 106; traffic control devices located downstream of long-distance vehicle 106; stop signs located downstream of long-distance vehicle 106; bus stops located downstream of long-distance vehicle 106; mailboxes located downstream of long-distance vehicle 106; business delivery entrances located downstream of long-distance vehicle 106; train crossings located downstream of long-distance vehicle 106; and / or roadside garbage collection points located downstream of long-distance vehicle 106, etc.

[0036] System 102 also includes a notification device 168 (e.g., a human-machine interface (HMI), a speaker, a display device such as an augmented reality head-up display (ARHUD), etc.) for providing a notification to the user (e.g., a displayed message, verbal announcement, and / or displayed symbol associated with a notification such as “overtaking a traffic obstacle”) to indicate that the distant vehicle 106 is a traffic obstacle associated with a predicted deceleration event.

[0037] System 102 also includes a computer 104, which includes one or more processors 170 electronically connected to a longitudinal control submodule 112, a lateral control submodule 118, an input device 122, and a notification device 168. Computer 104 also includes a non-transitory computer-readable storage medium (CRM) 172 storing instructions that program the processors 170 to receive input signals from at least one input device 122.

[0038] Processor 170 is also programmed to determine the speed profile of remote vehicle 106 based on the input signal. More specifically, in a non-limiting example, processor 170 may also be programmed to determine the category of remote vehicle 106 (e.g., passenger car 108, light truck, heavy truck, SUV, van, motorhome, bus, commercial truck 110, waste collection vehicle, multi-purpose vehicle, delivery vehicle, emergency vehicle, etc.) in response to processor 170 determining the category of remote vehicle 106. Processor 170 is also programmed to compare the category of remote vehicle 106 with a lookup table (e.g., in a remote database, etc.) in response to processor 170 determining the category of remote vehicle 106, the lookup table including multiple predetermined categories and multiple associated empirical speed profiles (e.g., average acceleration, estimated weight, average speed, and / or maximum speed associated with each category; predicted frequencies of stops such as multiple bus stop locations associated with a bus, multiple roadside garbage collection points associated with a waste collection vehicle, etc.).

[0039] In another non-limiting example, processor 170 may also be programmed to determine one or more surrounding vehicles 174 in response to receiving an input signal from input device 122. Figure 3 The distant vehicle 106 and the surrounding vehicles 174 are located in a common area and have a common brand, common model, common model year, common engine type and / or common engine size, etc. The processor 170 can also be programmed to determine the surrounding speed profile of the surrounding vehicles 174 and limit the speed profile of the distant vehicle 106 to the surrounding speed profile of the surrounding vehicles 174.

[0040] Processor 170 is also programmed to determine that the distant vehicle 106 is a traffic obstacle based on the input signal. In a non-limiting example, processor 170 is also programmed to compare the predicted stopping frequency of the distant vehicle 106 with a stopping frequency threshold, and to determine that the distant vehicle 106 is a traffic obstacle in response to processor 170 determining that the predicted stopping frequency is higher than the stopping frequency threshold. Processor 170 is also programmed to compare the average acceleration of the distant vehicle 106 with an acceleration threshold, and to determine that the distant vehicle 106 is a traffic obstacle in response to processor 170 determining that the average acceleration is lower than the acceleration threshold. Processor 170 is also programmed to compare the estimated weight of the distant vehicle 106 with a predetermined weight threshold, and to determine that the distant vehicle 106 is a traffic obstacle in response to processor 170 determining that the estimated weight is higher than the predetermined weight threshold. Processor 170 is also programmed to compare the average speed of the distant vehicle 106 with a predetermined average speed threshold, and to determine that the distant vehicle 106 is a traffic obstacle in response to processor 170 determining that the average speed is lower than the predetermined average speed threshold. The processor 170 is also programmed to compare the maximum speed of the distant vehicle 106 with a speed threshold, and to determine that the distant vehicle 106 is a traffic obstacle in response to the processor 170 determining that the maximum speed is below the speed threshold. It is conceivable that the processor can determine that a distant vehicle is a traffic obstacle based on input signals associated with any combination of the parameters described above, other suitable parameters, and associated thresholds.

[0041] Processor 170 is also programmed to determine a predicted deceleration event associated with the traffic obstacle and speed curve based on the input signal. More specifically, in a non-limiting example, processor 170 is also programmed to determine the predicted deceleration event in response to processor 170 determining that the distant vehicle 106 is a traffic obstacle and also based on the input signal associated with at least one of the following: an intersection 166 located downstream of the distant vehicle and within a predetermined distance D from the distant vehicle 106. Figure 2The following are listed: a highway entrance ramp located downstream of vehicle 106 and within a predetermined distance from vehicle 106; a highway exit ramp located downstream of vehicle 106 and within a predetermined distance from vehicle 106; a traffic control device located downstream of vehicle 106 and within a predetermined distance from vehicle 106; a stop sign located downstream of vehicle 106 and within a predetermined distance from vehicle 106; a bus stop located downstream of vehicle 106 and within a predetermined distance from vehicle 106; a mailbox located downstream of vehicle 106 and within a predetermined distance from vehicle 106; a business delivery entrance located downstream of vehicle 106 and within a predetermined distance from vehicle 106; a train crossing located downstream of vehicle 106 and within a predetermined distance from vehicle 106; and a roadside garbage collection point located downstream of vehicle 106 and within a predetermined distance from vehicle 106. It is conceivable that the processor could be programmed to determine the predicted deceleration event based on the input signal associated with any combination of these parameters, other suitable parameters, and associated thresholds.

[0042] Processor 170 is also programmed to generate actuation signals in response to processor 170 determining speed profiles, traffic obstacles, and predicted deceleration events. More specifically, in this non-limiting example, processor 170 is also programmed to determine, in response to processor 170 receiving an input signal, that autonomous vehicle 100 should navigate to exceed target lane 176 of distant vehicle 106. Figure 2 The processor 170 is also programmed to determine an obstruction level associated with the target lane 176 in response to receiving an input signal from the input device 122. The processor 170 is also programmed to compare the obstruction level with a predetermined obstruction threshold. The processor 170 is also programmed to determine that the target lane 176 is unobstructed in response to determining that the obstruction level is below the predetermined obstruction threshold. The processor 170 is also programmed to generate an actuation signal in response to determining that the target lane 176 is unobstructed. The processor 170 is also programmed to determine that an obstruction exists in the target lane 176 in response to determining that the obstruction level is not below the predetermined obstruction threshold. The processor 170 is also programmed to avoid generating an actuation signal in response to determining that an obstruction exists in the target lane 176.

[0043] Processor 170 is also programmed to determine an impending turn 178 of autonomous vehicle 100 and an associated approach distance D between the impending turn 178 and the current position of distant vehicle 106. Processor 170 is also programmed to compare the associated approach distance D with a approach distance threshold. Processor 170 is also programmed to generate an actuation signal in response to processor 170 determining that the associated approach distance D is higher than the approach distance threshold. Processor 170 is also programmed to avoid generating an actuation signal in response to processor 170 determining that the associated approach distance D is not higher than the approach distance threshold.

[0044] Notification device 168 provides a notification to the user in response to receiving an actuation signal from processor 170, indicating a predicted deceleration event. Longitudinal control submodule 112 controls propulsion actuator 114 and / or braking actuator 116 in response to receiving an actuation signal from processor 170, enabling autonomous vehicle 100 to overtake distant vehicle 106. Lateral control submodule 118 controls steering actuator 120 in response to receiving an actuation signal from processor 170, enabling autonomous vehicle 100 to overtake distant vehicle 106.

[0045] Reference Figure 4 It provides a way to operate Figure 1 Method 200 of system 102. Method 200 begins at block 202, where an input signal associated with the remote vehicle 106 is generated via input device 122. Method 200 proceeds to block 204.

[0046] In block 204, method 200 also includes receiving an input signal from input device 122 via processor 170 of computer 104. Method 200 proceeds to block 206.

[0047] At block 206, method 200 further includes determining a speed profile of the remote vehicle 106 based on the input signal by processor 170. In this non-limiting example, the speed profile can be determined by blocks 206 to 210 and / or blocks 234 to 238. More specifically, method 200 includes determining the category of the remote vehicle 106 by processor 170 in response to receiving an input signal from input device 122. Method 200 proceeds to block 208.

[0048] In box 208, method 200 further includes, in response to processor 170 determining the category of the remote vehicle, comparing the category of remote vehicle 106 with a lookup table, the lookup table including predetermined categories and associated empirical speed curves. In response to processor 170 identifying a predetermined category in the lookup table that matches the category of remote vehicle 106, method 200 proceeds to box 210. In response to processor 170 not identifying a predetermined category in the lookup table that matches the category of remote vehicle 106, method 200 proceeds to box 234.

[0049] At block 210, method 200 further includes determining the speed profile of remote vehicle 106 by processor 170 by defining the speed profile of remote vehicle 106 as a predetermined speed profile of a predetermined category that matches the category of remote vehicle 106. Method 200 proceeds to block 212.

[0050] In block 212, method 200 further includes determining, by processor 170, that a distant vehicle 106 is a traffic obstacle based on the input signal. Processor 170 may determine that a distant vehicle 106 is a traffic obstacle via blocks 212, 214, and blocks 242 to 246. More specifically, in this non-limiting example, method 200 includes comparing a predicted stopping frequency of the distant vehicle 106 with a stopping frequency threshold by processor 170. In response to processor 170 determining that the predicted stopping frequency is higher than the stopping frequency threshold, method 200 proceeds to block 214. In response to processor 170 determining that the predicted stopping frequency is not higher than the stopping frequency threshold, method 200 proceeds to block 240.

[0051] In box 214, method 200 also includes determining, via processor 170, that the distant vehicle 106 is a traffic obstacle. Method 200 proceeds to box 216.

[0052] In block 216, method 200 further includes determining a predicted deceleration event (e.g., at a location downstream of the current position of the distant vehicle 106) by processor 170 in response to processor 170 determining that the distant vehicle 106 is a traffic obstacle and also based on the input signal and associated with one or more conditions in blocks 216, 218, and 248 to 282. Method 200 may include comparing an intersection value (e.g., based on the input signal) with an intersection threshold by processor 170. In response to processor 170 determining that the intersection value is higher than the intersection threshold, method 200 proceeds to block 218. In response to processor 170 determining that the intersection value is not higher than the intersection threshold, method 200 proceeds to block 248.

[0053] In box 218, method 200 includes determining intersection 166 by processor 170. Figure 2Located downstream of the distant vehicle 106 and within a predetermined distance D from the distant vehicle 106, and associated with the predicted deceleration event. Method 200 proceeds to box 220.

[0054] In box 220, method 200 also includes determining, in response to processor 170 receiving an input signal, that autonomous vehicle 100 needs to navigate to exceed target lane 176 of distant vehicle 106. Figure 2 Method 200 proceeds to box 222.

[0055] In block 222, method 200 further includes determining, by processor 170, an obstruction level associated with target lane 176 in response to processor 170 receiving an input signal from input device 122. Method 200 also includes comparing the obstruction level with a predetermined obstruction threshold by processor 170. In response to processor 170 determining that the obstruction level is below the predetermined obstruction threshold, method 200 proceeds to block 224. In response to processor 170 determining that the obstruction level is not below the predetermined obstruction threshold, method 200 proceeds to block 284.

[0056] In box 224, method 200 further includes determining, by processor 170, that the target lane 176 is unobstructed in response to processor 170 determining that the obstruction level is below a predetermined obstruction threshold. Method 200 proceeds to box 226.

[0057] In box 226, method 200 also includes determining, by processor 170, an impending turn 178 of the autonomous vehicle 100 and an associated proximity distance D between the impending turn 178 and the current position of the distant vehicle 106. Method 200 proceeds to box 228.

[0058] In box 228, method 200 further includes comparing the associated proximity distance D with a proximity distance threshold via processor 170. In response to processor 170 determining that the proximity distance D is higher than the proximity distance threshold, method 200 proceeds to box 230. In response to processor 170 determining that the proximity distance D is not higher than the proximity distance threshold, method 200 proceeds to box 288.

[0059] In block 230, method 200 further includes generating an actuation signal by processor 170 in response to processor 170 determining a speed profile, traffic obstacles, and a predicted deceleration event, and also in response to processor 170 determining that the target lane 176 is free of obstacles, and also in response to processor 170 determining that the associated approach distance D is above an approach distance threshold. Method 200 proceeds to block 232.

[0060] In block 232, method 200 further includes providing a notification to a user via notification device 168 in response to receiving an actuation signal from processor 170, indicating a predicted deceleration event. Longitudinal control submodule 112 controls propulsion actuator 114 and / or braking actuator 116 in response to receiving an actuation signal from processor 170, enabling autonomous vehicle 100 to overtake distant vehicle 106. Lateral control submodule 118 controls steering actuator 120 in response to receiving an actuation signal from processor 170, enabling autonomous vehicle 100 to overtake distant vehicle 106. Method 200 proceeds to block 234.

[0061] In box 234, method 200 further includes determining surrounding vehicles 174 by processor 170 in response to processor 170 receiving an input signal from input device 122. Figure 3 The existence of a shared area where distant vehicle 106 and surrounding vehicle 174 are located in a common area and share a common brand, model, model year, engine type, and / or engine size. In response to processor 170 determining that distant vehicle 106 and surrounding vehicle 174 are located in a common area and share a common brand, model, model year, engine type, and / or engine size, method 200 proceeds to box 236. In response to processor 170 not determining that distant vehicle 106 and surrounding vehicle 174 are located in a common area and share a common brand, model, model year, engine type, and / or engine size, method 200 returns to box 202.

[0062] In box 236, method 200 further includes determining the surrounding speed profile of the surrounding vehicle 174 by processor 170 via, for example, a lookup table. Method 200 proceeds to box 238.

[0063] In box 238, method 200 further includes defining the velocity profile of the distant vehicle 106 as the surrounding velocity profile of the surrounding vehicle 174 by processor 170. Method 200 proceeds to box 212.

[0064] In box 240, method 200 further includes comparing the average acceleration of the remote vehicle 106 with an acceleration threshold by processor 170. In response to processor 170 determining that the average acceleration is below the acceleration threshold, method 200 proceeds to box 214. In response to processor 170 determining that the average acceleration is not below the acceleration threshold, method 200 proceeds to box 242.

[0065] In box 242, method 200 further includes comparing the estimated weight of the remote vehicle 106 with a predetermined weight threshold via processor 170. In response to processor 170 determining that the estimated weight is higher than the predetermined weight threshold, the method proceeds to box 214. In response to processor 170 determining that the estimated weight is not higher than the predetermined weight threshold, the method proceeds to box 244.

[0066] In block 244, method 200 further includes comparing the average speed of the remote vehicle 106 with a predetermined average speed threshold by processor 170. In response to processor 170 determining that the average speed is below the predetermined average speed threshold, method 200 proceeds to block 214. In response to processor 170 determining that the average speed is not below the predetermined average speed threshold, method 200 proceeds to block 246.

[0067] In box 246, method 200 further includes comparing the maximum speed of the distant vehicle 106 with a speed threshold. In response to processor 170 determining that the maximum speed is below the speed threshold, method 200 proceeds to box 214. In response to processor 170 determining that the maximum speed is not below the speed threshold, method 200 returns to box 202. It is conceivable that the processor can determine that the distant vehicle is a traffic obstacle based on input signals associated with any combination of the above parameters, other suitable parameters, and associated thresholds.

[0068] In block 248, method 200 further includes comparing a highway entrance ramp value (e.g., based on an input signal) with a predetermined highway entrance ramp threshold via processor 170. In response to processor 170 determining that the highway entrance ramp value is higher than the predetermined highway entrance ramp threshold, method 200 proceeds to block 250. In response to processor 170 determining that the highway entrance ramp value is not higher than the predetermined highway entrance ramp threshold, method 200 proceeds to block 252.

[0069] In block 250, method 200 further includes determining, via processor 170, that the highway entrance ramp is downstream of and within a predetermined distance D from distant vehicle 106, and is associated with a predicted deceleration event. Method 200 proceeds to block 220.

[0070] In block 252, method 200 further includes comparing a highway exit ramp value (e.g., based on an input signal) with a predetermined highway exit ramp threshold via processor 170. In response to processor 170 determining that the highway exit ramp value is higher than the predetermined highway exit ramp threshold, method 200 proceeds to block 254. In response to processor 170 determining that the highway exit ramp value is not higher than the predetermined highway exit ramp threshold, method 200 proceeds to block 256.

[0071] In block 254, method 200 further includes determining, via processor 170, that the highway exit ramp is downstream of and within a predetermined distance D from distant vehicle 106, and is associated with a predicted deceleration event. Method 200 proceeds to block 220.

[0072] In block 256, method 200 further includes comparing a traffic control value (e.g., based on an input signal) with a predetermined traffic control threshold via processor 170. In response to processor 170 determining that the traffic control value is higher than the traffic control threshold, method 200 proceeds to block 258. In response to processor 170 determining that the traffic control value is not higher than the predetermined traffic control threshold, method 200 proceeds to block 260.

[0073] In block 258, method 200 further includes determining, via processor 170, that the traffic control device is located downstream of and within a predetermined distance D from the remote vehicle 106, and is associated with a predicted deceleration event. Method 200 proceeds to block 220.

[0074] In block 260, method 200 further includes comparing a stop flag value (e.g., based on an input signal) with a predetermined stop flag threshold by processor 170. In response to processor 170 determining that the stop flag value is higher than the stop flag threshold, method 200 proceeds to block 262. In response to processor 170 determining that the stop flag value is not higher than the predetermined stop flag threshold, method 200 proceeds to block 264.

[0075] In block 262, method 200 further includes determining, via processor 170, that the stop sign is located downstream of and within a predetermined distance D from the distant vehicle 106, and is associated with a predicted deceleration event. Method 200 proceeds to block 220.

[0076] In box 264, method 200 further includes comparing a bus stop value (e.g., based on an input signal) with a predetermined bus stop threshold via processor 170. In response to processor 170 determining that the bus stop value is higher than a stop sign threshold, method 200 proceeds to box 266. In response to processor 170 determining that the bus stop value is not higher than the predetermined bus stop threshold, method 200 proceeds to box 268.

[0077] In box 266, method 200 further includes determining, via processor 170, that the bus stop is located downstream of and within a predetermined distance D from the distant vehicle 106, and is associated with a predicted deceleration event. Method 200 proceeds to box 220.

[0078] In box 268, method 200 further includes comparing a mailbox value (e.g., based on an input signal) with a predetermined mailbox threshold via processor 170. In response to processor 170 determining that the mailbox value is higher than the mailbox threshold, method 200 proceeds to box 270. In response to processor 170 determining that the mailbox value is not higher than the predetermined mailbox threshold, method 200 proceeds to box 272.

[0079] In block 270, method 200 further includes determining, via processor 170, that the mailbox is located downstream of and within a predetermined distance D from the remote vehicle 106, and is associated with a predicted deceleration event. Method 200 proceeds to block 220.

[0080] In box 272, method 200 further includes comparing a service delivery entry value (e.g., based on an input signal) with a predetermined service delivery entry threshold via processor 170. In response to processor 170 determining that the service delivery entry value is higher than the service delivery entry threshold, method 200 proceeds to box 274. In response to processor 170 determining that the service delivery entry value is not higher than the predetermined service delivery entry threshold, method 200 proceeds to box 276.

[0081] In box 274, method 200 further includes determining, via processor 170, that the business delivery entry is located downstream of remote vehicle 106 and within a predetermined distance D from remote vehicle 106, and is associated with a predicted deceleration event. Method 200 proceeds to box 220.

[0082] In block 276, method 200 further includes comparing a train crossing value (e.g., based on an input signal) with a predetermined train crossing threshold via processor 170. In response to processor 170 determining that the train crossing value is higher than the train crossing threshold, method 200 proceeds to block 278. In response to processor 170 determining that the train crossing value is not higher than the predetermined train crossing threshold, method 200 proceeds to block 280.

[0083] In block 278, method 200 further includes determining, via processor 170, that the train crossing is located downstream of and within a predetermined distance D from distant vehicle 106, and is associated with a predicted deceleration event. Method 200 proceeds to block 220.

[0084] In box 280, method 200 further includes comparing a curbside litter collection point value (e.g., based on an input signal) with a predetermined curbside litter collection point threshold via processor 170. In response to processor 170 determining that the curbside litter collection point value is higher than the curbside litter collection point threshold, method 200 proceeds to box 282. In response to processor 170 determining that the curbside litter collection point value is not higher than the predetermined curbside litter collection point threshold, method 200 returns to box 202.

[0085] In box 282, method 200 further includes determining, via processor 170, that the roadside garbage collection point is located downstream of and within a predetermined distance D from the distant vehicle 106, and is associated with a predicted deceleration event. Method 200 proceeds to box 220.

[0086] In box 284, method 200 further includes determining, in response to processor 170 determining that the obstruction level is not lower than a predetermined obstruction threshold, that an obstruction exists in target lane 176. Method 200 proceeds to box 286.

[0087] In box 286, method 200 further includes avoiding generating an actuation signal by processor 170 in response to processor 170 determining that an obstruction exists in target lane 176. Method 200 returns to box 202.

[0088] In box 288, method 200 further includes avoiding generating an actuation signal by processor 170 in response to processor 170 determining that the associated proximity distance D is not higher than a proximity distance threshold. Method 200 returns to box 202.

[0089] Computers and computing devices typically include computer-executable instructions, which can be executed by one or more computing devices such as those listed above. Computer-executable instructions can be compiled or interpreted by computer programs created using various programming languages ​​and / or technologies, including but not limited to (individual or combined forms): JAVA, C, C++, MATLAB, SIMULINK, STATEFLOW, VISUAL BASIC, JAVA SCRIPT, PERL, HTML, TENSORFLOW, PYTORCH, KERAS, etc. Some of these applications can be compiled and executed on virtual machines such as JAVA VIRTUALMACHINE, DALVIK Virtual Machine, etc. Typically, a processor (e.g., a microprocessor) receives instructions from memory, computer-readable media, etc., and executes these instructions to perform one or more processes, including one or more processes described herein. Various computer-readable media can be used to store and transfer such instructions and other data. Files in computing devices are typically collections of data stored on computer-readable media such as storage media, random access memory, etc.

[0090] The processor can be communicatively coupled, for example via a vehicle communication module, to more than one local processor, such as an electronic processor unit (ECU) included in an autonomous vehicle 100, for monitoring and / or controlling various vehicle components. The processor 170 is typically arranged to communicate on the vehicle communication module via an internal wired and / or wireless network (e.g., a bus in the autonomous vehicle 100, such as a controller area network (CAN) and / or other wired and / or wireless mechanisms). Via the vehicle communication module, the processor 170 can transmit messages to and / or receive messages from various devices in the autonomous vehicle 100, such as vehicle sensors, actuators, vehicle components, human-machine interfaces (HMIs), etc. Alternatively or additionally, where the processor includes multiple devices, the vehicle communication network can be used for communication between devices represented as a computer in this disclosure. Furthermore, various processors and / or vehicle sensors can provide data to the computer. The processor can receive and analyze data from the sensors substantially continuously and / or periodically. Furthermore, object classification or recognition technologies can be used, for example, in processors based on data from lidar sensors, camera sensors, etc., to identify lane markings, object types (e.g., vehicles, people, rocks, potholes, bicycles, motorcycles, etc.), and the physical characteristics of objects.

[0091] Memory can include computer-readable media (also known as processor-readable media), which includes any non-transitory (e.g., tangible) medium involved in providing data (e.g., instructions) that can be read by a computer (e.g., by the computer's processor). Such media can take many forms, including but not limited to non-volatile and volatile media. Non-volatile media can include, for example, optical discs or magnetic disks and other permanent storage. Volatile media can include, for example, dynamic random access memory (DRAM), which typically constitutes main memory. Such instructions can be transmitted via one or more transmission media, including coaxial cables, copper wires, and optical fibers; including lines containing a system bus connected to the processor of the ECU. Common forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic media, CD-ROMs, DVDs, any other optical media, punched cards, paper tape, any other physical media with a perforated pattern, RAM, PROM, EPROM, FLASH EEPROM, any other memory chip or cartridge memory, or any other medium that a computer can read.

[0092] The databases, data repositories, or other data stores described in this article can include various mechanisms for storing, accessing, and retrieving a wide range of data. These mechanisms include hierarchical databases, a set of files in a file system, application databases in proprietary formats, relational database management systems (RDBMS), and so on. Each such data store is typically contained within a computing device employing one of the computer operating systems mentioned above, and is accessed via a network in any one or more of various ways. File systems can be accessed from the computer operating system, and file systems can include files stored in various formats. In addition to using languages ​​for creating, storing, editing, and executing stored procedures, RDBMS typically employs a structured query language (SQL), such as the PL / SQL language described above.

[0093] In some examples, system elements may be implemented as computer-readable instructions (e.g., software) stored on one or more computing devices (e.g., servers, personal computers, etc.) and on an associated computer-readable medium (e.g., disks, storage devices, etc.). A computer program product may include such instructions stored on a computer-readable medium for performing the functions described herein.

[0094] Regarding the media, processes, systems, methods, heuristics, etc., described herein, it should be understood that although the steps of these processes, etc., are described as occurring in a certain ordered sequence, these processes can be practiced by performing the described steps in a different order than that described herein. It should also be understood that some steps may be performed simultaneously, other steps may be added, or some steps described herein may be omitted. In other words, the description of processes herein is provided for the purpose of illustrating certain embodiments and should in no way be construed as limiting the claims.

[0095] Therefore, it should be understood that the above description is intended to be illustrative rather than restrictive. After reading the above description, many embodiments and applications will be apparent to those skilled in the art, in addition to the examples provided. The scope of the invention should not be determined by reference to the above description, but rather by reference to the appended claims and the full scope of their equivalents. Future developments are anticipated and contemplated within the field discussed herein, and the disclosed systems and methods will be incorporated into these future embodiments. In summary, it should be understood that modifications and variations are possible with respect to the invention, and it is defined solely by the following claims.

[0096] All terms used in the claims are intended to be given their simple, ordinary meaning as understood by one of ordinary skill in the art, unless otherwise expressly indicated herein. In particular, the use of singular articles such as “a,” “the,” and “the” should be interpreted as referring to one or more of the indicated elements, unless the claims expressly limit this to the contrary.

[0097] The description in this disclosure is merely exemplary in nature, and changes that do not depart from the spirit and scope of this disclosure are intended to fall within its scope. These changes should not be considered as departing from the spirit and scope of this disclosure.

Claims

1. An autonomous vehicle, comprising: A longitudinal control submodule is used to control at least one of the propulsion actuator and the braking actuator of the autonomous vehicle. The lateral control submodule is used to control the steering actuator of the autonomous vehicle. as well as The system includes: At least one input device for generating an input signal associated with a remote vehicle; At least one notification device is used to provide a user with notification that the distant vehicle is a traffic obstacle associated with a predicted deceleration event; and A computer includes at least one processor electronically connected to the longitudinal control submodule, the lateral control submodule, the at least one input device, and the at least one notification device, and the computer further includes a non-transitory computer-readable storage medium (CRM) storing instructions that program the at least one processor to: Receive the input signal from the at least one input device; The speed profile of the distant vehicle is determined based on the input signal; Based on the input signal, it is determined that the distant vehicle is a traffic obstacle; Based on the input signal, a predicted deceleration event associated with the traffic obstacle and the speed profile is determined; and In response to the at least one processor determining the traffic obstacle, the speed profile, and the predicted deceleration event, an actuation signal is generated to cause the autonomous vehicle to overtake the distant vehicle; and The at least one notification device provides the user with a notification that the distant vehicle is a traffic obstacle associated with a predicted deceleration event in response to receiving the actuation signal from the at least one processor. Wherein, the speed curve of the distant vehicle indicates the predicted stopping frequency, and the at least one processor is further programmed to: The predicted stopping frequency of the long-distance vehicle is compared with a stopping frequency threshold; and In response to the at least one processor determining that the predicted stopping frequency is higher than the stopping frequency threshold, the distant vehicle is determined to be a traffic obstacle; Wherein, the speed curve indicates the average acceleration of the distant vehicle, and the at least one processor is further programmed to: Compare the average acceleration of the distant vehicle with an acceleration threshold; and In response to the at least one processor determining that the average acceleration is below the acceleration threshold, the distant vehicle is determined to be a traffic obstacle; Wherein, the speed curve indicates the estimated weight of the long-distance vehicle, and the at least one processor is further programmed to: The estimated weight of the long-distance vehicle is compared with a predetermined weight threshold; and In response to the at least one processor determining that the estimated weight is higher than the predetermined weight threshold, the distant vehicle is determined to be a traffic obstacle; Wherein, the speed curve indicates the average speed of the distant vehicle, and the at least one processor is further programmed to: The average speed of the distant vehicle is compared with an average speed threshold; and In response to the at least one processor determining that the average speed is below the average speed threshold, the distant vehicle is determined to be a traffic obstacle.

2. The autonomous vehicle according to claim 1, wherein, The longitudinal control submodule controls at least one of the propulsion actuator and the braking actuator in response to receiving the actuation signal from the at least one processor, so that the autonomous vehicle can overtake the distant vehicle, and the lateral control submodule controls the steering actuator in response to receiving the actuation signal from the at least one processor, so that the autonomous vehicle can overtake the distant vehicle.

3. The autonomous vehicle according to claim 2, wherein, The at least one processor is also programmed to: The at least one processor determines the category of the long-distance vehicle in response to receiving the input signal from the at least one input device; In response to the at least one processor determining the category of the long-distance vehicle, the category of the long-distance vehicle is compared with a lookup table, the lookup table including multiple predetermined categories and multiple associated empirical speed curves; as well as The speed profile of the long-distance vehicle is determined in response to the at least one processor comparing the category of the long-distance vehicle with the lookup table.

4. The autonomous vehicle according to claim 2, wherein, The at least one processor is also programmed to: In response to the at least one processor receiving the input signal from the at least one input device, at least one surrounding vehicle is determined, wherein the distant vehicle and the at least one surrounding vehicle are located in a common area and have at least one of the following: common brand, common model, common model year, common engine type, and common engine size; Determine the surrounding speed profiles of the at least one surrounding vehicle; and The speed profile of the distant vehicle is defined as the speed profile of the surrounding vehicles of the at least one surrounding vehicle.

5. The autonomous vehicle according to claim 2, wherein, The at least one processor is also programmed to determine the predicted deceleration event in response to the at least one processor determining that the distant vehicle is a traffic obstacle, and also based on the input signal associated with at least one of the following: The intersection located downstream of the aforementioned distant vehicle; The highway entrance ramp located downstream of the aforementioned distant vehicle; The highway exit ramp located downstream of the aforementioned distant vehicle; Traffic control device located downstream of the aforementioned distant vehicle; A stop sign located downstream of the distant vehicle; A bus stop located downstream of the aforementioned distant vehicle; A mailbox located downstream of the aforementioned distant vehicle; The business delivery entrance is located downstream of the aforementioned long-distance vehicle; The train crossing located downstream of the aforementioned distant vehicle; as well as A roadside garbage collection point located downstream of the aforementioned distant vehicle.

6. The autonomous vehicle according to claim 2, wherein, The at least one processor is also programmed to: In response to the processor receiving the input signal, it determines that the autonomous vehicle needs to navigate to overtake the target lane of the distant vehicle; In response to receiving the input signal from the input device, the processor determines the level of obstruction associated with the target lane; The obstruction level is compared with a predetermined obstruction threshold; In response to the at least one processor determining that the obstruction level is below the predetermined obstruction threshold, it is determined that the target lane is unobstructed; The actuation signal is also generated in response to the at least one processor determining that the target lane is unobstructed; The processor determines that an obstruction exists in the target lane in response to determining that the obstruction level is not lower than the predetermined obstruction threshold. as well as The actuation signal is avoided in response to the at least one processor determining that there is an obstruction in the target lane.

7. The autonomous vehicle according to claim 2, wherein, The at least one processor is also programmed to: Determine the upcoming turn of the autonomous vehicle and the associated proximity distance between the upcoming turn and the current position of the distant vehicle; The associated proximity distance is compared with a proximity distance threshold; The actuation signal is also generated in response to the at least one processor determining that the associated proximity distance is higher than the proximity distance threshold; as well as The at least one processor avoids generating the actuation signal in response to determining that the associated proximity distance is not higher than the proximity distance threshold.

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