Patent Publication Number: US-2018052470-A1

Title: Obstacle Avoidance Co-Pilot For Autonomous Vehicles

Description:
TECHNICAL FIELD 
     The present disclosure relates to vehicles controlled by automated driving systems, particularly those configured to automatically control vehicle steering, acceleration, and braking during a drive cycle without human intervention. 
     INTRODUCTION 
     The operation of modern vehicles is becoming more automated, i.e. able to provide driving control with less and less driver intervention. Vehicle automation has been categorized into numerical levels ranging from Zero, corresponding to no automation with full human control, to Five, corresponding to full automation with no human control. Various automated driver-assistance systems, such as cruise control, adaptive cruise control, and parking assistance systems correspond to lower automation levels, while true “driverless” vehicles correspond to higher automation levels. 
     SUMMARY 
     An automotive vehicle according to the present disclosure includes a vehicle steering system, an actuator configured to control the steering system, and first and second controllers. The first controller is in communication with the actuator. The first controller is programmed with a primary automated driving system control algorithm and is configured to communicate an actuator control signal based on the primary automated driving system control algorithm. The second controller is in communication with the actuator and with the first controller. The second controller is configured to, in response to a first predicted vehicle path based on the actuator control signal passing within a first threshold distance of a detected obstacle, control the actuator to maintain a current actuator setting. The second controller is also configured to in response to the first predicted vehicle path not passing within the first threshold distance of a detected obstacle, control the actuator according to the actuator control signal. 
     According to at least one embodiment, the second controller is further configured to, in response to a second predicted vehicle path based on the current actuator setting passing within a second threshold distance of a detected obstacle, control the actuator based on a fallback command. In such embodiments, the second controller may be configured to predict a first relative distance between the detected obstacle and the first predicted vehicle path and to predict a second relative distance between the detected obstacle and the second predicted vehicle path. 
     According to at least one embodiment, the second controller is configured to predict the first vehicle path based on the actuator control signal in response to the actuator control signal. 
     According to at least one embodiment, the first controller is associated with a first CPU and the second controller is associated with a second CPU. 
     According to at least one embodiment, the vehicle further includes a second actuator configured to control a vehicle throttle, a third actuator configured to control vehicle brakes, and a fourth actuator configured to control vehicle shifting. In such embodiments, the controller is additionally in communication with the second actuator, third actuator, and fourth actuator. 
     A method of controlling a vehicle according to the present disclosure includes providing the vehicle with an actuator configured to control vehicle steering, throttle, braking, or shifting. The method additionally includes providing the vehicle with a first controller in communication with the actuator and having a primary automated driving system control algorithm. The method also includes providing the vehicle with a second controller in communication with the actuator and the first controller. The method further includes communicating, from the first controller, an actuator control signal based on the primary automated driving system control algorithm. The method still further includes, in response to a first predicted vehicle path based on the actuator control signal passing within a first threshold distance of a detected obstacle, controlling, by the second controller, the actuator to maintain a current actuator setting. 
     According to at least one embodiment, the method additionally includes, in response to the first predicted vehicle path not passing within the first threshold distance of the detected obstacle, controlling the actuator based on the actuator control signal. 
     According to at least one embodiment, the method additionally includes, in response to a second predicted vehicle path based on the current actuator setting passing within a second threshold distance of a detected obstacle, controlling the actuator based on a fallback command. Such embodiments may additionally include predicting, by the second controller, a first relative distance between the detected obstacle and the first predicted vehicle path, and predicting, by the second controller, a second relative distance between the detected obstacle and the second predicted vehicle path. 
     A system for autonomous control of a vehicle according to the present disclosure includes an actuator configured to control vehicle steering, throttle, braking, or shifting. The system additionally includes a first controller in communication with the actuator. The first controller is configured to communicate an actuator control signal based on a primary automated driving system control algorithm. The system further includes a second controller in communication with the actuator and with the first controller. The second controller is configured to, in response to a first predicted vehicle path based on the actuator control signal passing within a first threshold distance of a detected obstacle, control the actuator to maintain a current actuator setting. 
     According to at least one embodiment, the second controller is further configured to, in response to a second predicted vehicle path based on the current actuator setting passing within a second threshold distance of a detected obstacle, control the actuator based on a fallback command. In such embodiments, the second controller may be configured to predict a first relative distance between the detected obstacle and the first predicted vehicle path and to predict a second relative distance between the detected obstacle and the second predicted vehicle path. 
     According to at least one embodiment, the second controller is configured to predict the first vehicle path based on the actuator control signal in response to the actuator control signal. 
     According to at least one embodiment, the first controller is associated with a first CPU and the second controller is associated with a second CPU. 
     According to at least one embodiment, the actuator is configured to control vehicle steering. In such embodiments, the system further includes a second actuator configured to control a vehicle throttle, a third actuator configured to control vehicle brakes, and a fourth actuator configured to control vehicle shifting. In such embodiments, the controller is additionally in communication with the second actuator, third actuator, and fourth actuator. 
     Embodiments according to the present disclosure provide a number of advantages. For example, embodiments according to the present disclosure may enable independent validation of autonomous vehicle control commands to aid in diagnosis of software or hardware conditions in the primary control system. Embodiments according to the present disclosure may thus be more robust, increasing customer satisfaction. 
     The above advantage and other advantages and features of the present disclosure will be apparent from the following detailed description of the preferred embodiments when taken in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic representation of a vehicle according to the present disclosure; 
         FIG. 2  is a schematic representation of a first embodiment of a system for controlling a vehicle according to the present disclosure; 
         FIG. 3  is a schematic representation of a second embodiment of a system for controlling a vehicle according to the present disclosure; and 
         FIG. 4  is a flowchart representation of a method for controlling a vehicle according to the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present disclosure are described herein. It is to be understood, however, that the disclosed embodiments are merely examples and other embodiments can take various and alternative forms. The figures are not necessarily to scale; some features could be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention. As those of ordinary skill in the art will understand, various features illustrated and described with reference to any one of the figures can be combined with features illustrated in one or more other figures to produce embodiments that are not explicitly illustrated or described. The combinations of features illustrated provide representative embodiments for typical applications. Various combinations and modifications of the features consistent with the teachings of this disclosure, however, could be desired for particular applications or implementations. 
     Referring now to  FIG. 1 , an automotive vehicle  10  according to the present disclosure is shown in schematic form. The automotive vehicle  10  includes a propulsion system  12 , which may in various embodiments include an internal combustion engine, an electric machine such as a traction motor, and/or a fuel cell propulsion system. 
     The automotive vehicle  10  also includes a transmission  14  configured to transmit power from the propulsion system  12  to vehicle wheels  16  according to selectable speed ratios. According to various embodiments, the transmission  14  may include a step-ratio automatic transmission, a continuously-variable transmission, or other appropriate transmission. 
     The automotive vehicle  10  additionally includes a steering system  18 . While depicted as including a steering wheel for illustrative purposes, in some embodiments contemplated within the scope of the present disclosure, the steering system  18  may not include a steering wheel. 
     The automotive vehicle  10  additionally includes a plurality of vehicle wheels  16  and associated wheel brakes  20  configured to provide braking torque to the vehicle wheels  16 . The wheel brakes  20  may, in various embodiments, include friction brakes, a regenerative braking system such as an electric machine, and/or other appropriate braking systems. 
     The propulsion system  12 , transmission  14 , steering system  18 , and wheel brakes  20  are in communication with or under the control of at least one controller  22 . While depicted as a single unit for illustrative purposes, the controller  22  may additionally include one or more other controllers, collectively referred to as a “controller.” The controller  22  may include a microprocessor or central processing unit (CPU) in communication with various types of computer readable storage devices or media. Computer readable storage devices or media may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example. KAM is a persistent or non-volatile memory that may be used to store various operating variables while the CPU is powered down. Computer-readable storage devices or media may be implemented using any of a number of known memory devices such as PROMs (programmable read-only memory), EPROMs (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or any other electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the controller  22  in controlling the vehicle. 
     The controller  22  is provided with an automated driving system (ADS)  24  for automatically controlling various actuators in the vehicle  10 . In an exemplary embodiment, the ADS  24  is configured to control the propulsion system  12 , transmission  14 , steering system  18 , and wheel brakes  20  to control vehicle acceleration, steering, and braking, respectively, without human intervention. 
     The ADS  24  is configured to control the propulsion system  12 , transmission  14 , steering system  18 , and wheel brakes  20  in response to inputs from a plurality of sensors  26 , which may include GPS, RADAR, LIDAR, optical cameras, thermal cameras, ultrasonic sensors, and/or additional sensors as appropriate. 
     The vehicle  10  additionally includes a wireless communications system  28  configured to wirelessly communicate with other vehicles (“V2V”) and/or infrastructure (“V2I”). In an exemplary embodiment, the wireless communication system  28  is configured to communicate via a dedicated short-range communications (DSRC) channel. DSRC channels refer to one-way or two-way short-range to medium-range wireless communication channels specifically designed for automotive use and a corresponding set of protocols and standards. However, additional or alternate wireless communications standards, such as IEEE 802.11 and cellular data communication, are also considered within the scope of the present disclosure. 
     In an exemplary embodiment, the ADS  24  is a so-called Level Four or Level Five automation system. A Level Four system indicates “high automation”, referring to the driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task, even if a human driver does not respond appropriately to a request to intervene. A Level Five system indicates “full automation”, referring to the full-time performance by an automated driving system of all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a human driver. 
     Referring now to  FIG. 2 , an exemplary architecture for an ADS  24 ′ according to the present disclosure is illustrated. The ADS  24 ′ may be provided via one or more controllers as illustrated in  FIG. 1  and discussed in further detail below. 
     The ADS  24 ′ includes multiple distinct control systems, as will be discussed in further detail below. Among the multiple distinct control systems is at least one primary control system  30 . 
     The primary control system  30  includes a sensor fusion module  32  for determining the presence, location, and path of detected features in the vicinity of the vehicle. The sensor fusion module  32  is configured to receive inputs from a variety of sensors, such as the sensors  26  illustrated in  FIG. 1 . The sensor fusion module  32  processes and synthesizes the inputs from the variety of sensors and generates a sensor fusion output  34 . The sensor fusion output  34  includes various calculated parameters including, but not limited to, a location of a detected obstacle relative to the vehicle, a predicted path of the detected obstacle relative to the vehicle, and a location and orientation of traffic lanes relative to the vehicle. 
     The primary control system  30  also includes a mapping and localization module  36  for determining the location of the vehicle and route for a current drive cycle. The mapping and localization module  36  is also configured to receive inputs from a variety of sensors, such as the sensors  26  illustrated in  FIG. 1 . The mapping and localization module  36  processes and synthesizes the inputs from the variety of sensors, and generates a mapping and localization output  38 . The mapping and localization output  38  includes various calculated parameters including, but not limited to, a vehicle route for the current drive cycle, and a current vehicle location relative to the route. In addition, the mapping and localization module  36  generates a vehicle location output  40 . The vehicle location output  40  includes the current vehicle location relative to the route, and is used in a separate calculation as will be discussed below. 
     The primary control system  30  additionally includes a path planning module  42  for determining a vehicle path to be followed to maintain the vehicle on the desired route while obeying traffic laws and avoiding any detected obstacles. The path planning module  42  employs a first obstacle avoidance algorithm configured to avoid any detected obstacles in the vicinity of the vehicle, a first lane keeping algorithm configured to maintain the vehicle in a current traffic lane, and a first route keeping algorithm configured to maintain the vehicle on the desired route. The path planning module  42  is configured to receive the sensor fusion output  34  and the mapping and localization output  38 . The path planning module  42  processes and synthesizes the sensor fusion output  34  and the mapping and localization output  38 , and generates a path planning output  44 . The path planning output  44  includes a commanded vehicle path based on the vehicle route, vehicle location relative to the route, location and orientation of traffic lanes, and the presence and path of any detected obstacles. 
     The primary control system  30  further includes a vehicle control module  46  for issuing control commands to vehicle actuators. The vehicle control module employs a first path algorithm for calculating a vehicle path resulting from a given set of actuator settings. The vehicle control module  46  is configured to receive the path planning output  44 . The vehicle control module  46  processes the path planning output  44  and generates a vehicle control output  48 . The vehicle control output  48  includes a set of actuator commands to achieve the commanded path from the vehicle control module  46 , including but not limited to a steering command, a shift command, a throttle command, and a brake command. 
     The vehicle control output  48  is communicated to actuators  50 . In an exemplary embodiment, the actuators  50  include a steering control, a shifter control, a throttle control, and a brake control. The steering control may, for example, control a steering system  18  as illustrated in  FIG. 1 . The shifter control may, for example, control a transmission  14  as illustrated in  FIG. 1 . The throttle control may, for example, control a propulsion system  12  as illustrated in  FIG. 1 . The brake control may, for example, control wheel brakes  20  as illustrated in  FIG. 1 . 
     In addition to the primary control system  30 , the ADS  24 ′ also includes at least one orthogonal co-pilot system  52 . The orthogonal co-pilot system  52  is configured to verify and, if necessary, override the operation of the primary control system  30  using distinct algorithms from those employed in the primary control system  30 . 
     The orthogonal co-pilot system  52  includes a path calculation module  54 . The path calculation module  54  is configured to receive the vehicle location output  40  and the vehicle control output  48 . The path calculation module  54  processes and synthesizes the vehicle location output  40  and the vehicle control output  48 , and generates a path calculation output  58 . The path calculation output  58  includes a first predicted path based on the path planning output  44  and a second predicted path based on current actuator settings in the absence of the path planning output  44 . The path calculation module  54  includes a vehicle model  56  and employs a second path algorithm, which are distinct from the first path algorithm used in the vehicle control module  46 . 
     The orthogonal co-pilot system  52  also includes an obstacle avoidance verification module  60 . The obstacle avoidance verification module  60  is provided to verify that the vehicle  10  maintains a desired distance from any detected obstacles, such as other vehicles and/or roadside objects. The obstacle avoidance verification module  60  is configured to receive the path calculation output  58  and the sensor fusion output  34 . The obstacle avoidance verification module  60  processes and synthesizes the path calculation output  58  and the sensor fusion output  34  and generates an obstacle avoidance verification output  62 . The obstacle avoidance verification output  62  may include a Boolean true/false signal or other appropriate signal indicating the presence or absence of an obstacle in the first predicted path and/or in the second predicted path. The obstacle avoidance verification module  60  employs a second obstacle avoidance algorithm, which is distinct from the first obstacle avoidance algorithm used in the path planning module  42 . 
     The orthogonal co-pilot system  52  additionally includes a lane keeping verification module  64 . The lane keeping verification module  64  is provided to maintain the vehicle in a desired traffic lane. The lane keeping verification module  64  is configured to receive the path calculation output  58  and the sensor fusion output  34 . The lane keeping verification module  64  processes and synthesizes the path calculation output  58  and the sensor fusion output  34  and generates a lane keeping verification output  66 . The lane keeping verification output  66  may include a Boolean true/false signal or other appropriate signal indicating whether the first predicted path and/or the second predicted path would maintain the vehicle in a current traffic lane. The lane keeping verification module  64  employs a second lane keeping algorithm, which is distinct from the first lane keeping algorithm used in the path planning module  42 . 
     The orthogonal co-pilot system  52  further includes a route keeping verification module  68 . The route keeping verification module  68  is provided to maintain the vehicle on a desired route and within an authorized operating environment. The route keeping verification module  68  is configured to receive the path calculation output  58  and the mapping and localization output  38 . The route keeping verification module  68  processes and synthesizes the path calculation output  58  and the mapping and localization output  38  and generates a route keeping verification output  70 . The route keeping verification output  70  may include a Boolean true/false signal or other appropriate signal indicating whether the first predicted path and/or the second predicted path would maintain the vehicle on the route for the current drive cycle. The route keeping verification module  68  employs a second route keeping algorithm, which is distinct from the first route keeping algorithm used in the path planning module  42 . 
     The orthogonal co-pilot system  52  further includes an arbitration module  72 . The arbitration module  72  is configured to receive the obstacle avoidance verification output  62 , the lane keeping verification output  66 , and the route keeping verification output  70 . The arbitration module processes and synthesizes the obstacle avoidance verification output  62 , the lane keeping verification output  66 , and the route keeping verification output  70 , and outputs an orthogonal control output  74 . The orthogonal control output  74  may include a signal to accept the vehicle control output  48 , a signal to modify the vehicle control output  48 , or a signal to reject the vehicle control output  48 . 
     By providing the orthogonal co-pilot system  52  with algorithms distinct from those employed in the primary control system  30 , the commanded path and actuator control signals may be validated independently from any software diagnostic conditions arising in the primary control system  30 . 
     Referring now to  FIG. 3 , an exemplary architecture for a controller  22 ′ according to the present disclosure is illustrated schematically. The controller  22 ′ includes at least one primary microprocessor  80  and associated non-transient data storage provided with a primary control system  30 ′, which may be configured generally similarly to the primary control system  30  illustrated in  FIG. 2 . In the exemplary embodiment of  FIG. 3 , multiple primary microprocessors  80  are provided, each with associated non-transient data storage having a primary control system  30 ′. In addition, at least one orthogonal microprocessor  82  is provided, distinct from the one or more primary microprocessors  80 . The orthogonal microprocessor  82  is provided with associated non-transient data storage having an orthogonal co-pilot system  52 ′, which may be configured generally similarly to the orthogonal co-pilot system  52  illustrated in  FIG. 2 . Vehicle actuators  50 ′ are under the collective control of the one or more primary microprocessors  80  and the at least one orthogonal microprocessor  82 . 
     By providing the orthogonal co-pilot system  52 ′ on a distinct hardware from that of the primary control system  30 ′, the commanded path and actuator control signals may be validated independently from any hardware diagnostic conditions arising in the one or more primary microprocessors  80 . 
     Referring now to  FIG. 4 , an exemplary embodiment of an obstacle avoidance verification algorithm, e.g. as may be used in the obstacle avoidance verification module  60 , is illustrated in flowchart form. 
     The algorithm begins with an obstacle optimization phase  100 . Path calculation output and sensor fusion output are received, as illustrated at block  102 . As discussed above, path calculation output includes a first predicted path based on the path planning output and a second predicted path based on current actuator settings in the absence of the path planning output, while sensor fusion output may include various calculated parameters including, but not limited to, a location of a detected obstacle relative to the vehicle, a predicted path of the detected obstacle relative to the vehicle, and a location and orientation of traffic lanes relative to the vehicle. 
     A relative distance is calculated between the vehicle and detected obstacles at their current positions, as illustrated at  104 . The relative distance may be calculated based on, for example, locations of detected obstacles included in the sensor fusion output. 
     A reduced obstacle list is defined, as illustrated at block  106 . The reduced obstacle list includes a subset of the obstacles from the sensor fusion output for which the relative distance is less than a first evaluation distance minDist1. The evaluation distance minDist1 is a calibratable parameter corresponding to a range within which obstacles are to be evaluated. Thus, distant obstacles need not be evaluated, reducing computing resource requirements. In an exemplary embodiment, minDist1 is a variable based on current vehicle speed, such that at higher speeds, minDist1 has a higher value. 
     Control then proceeds to a commanded path evaluation phase  108 . In the commanded path evaluation phase  108 , the first predicted path based on the path planning output is evaluated to verify that the path planning output would not result in the host vehicle contacting an obstacle. 
     A first time counter t_cp is initialized to zero, as illustrated at block  110 . As will be discussed in further detail below, the first time counter t_cp corresponds to a temporal window for prediction of vehicle and obstacle locations relative to a predicted path based on commanded actuator settings. 
     A determination is made of whether t_cp is greater than or equal to a maximum evaluation time maxTime, as illustrated at operation  112 . The maximum evaluation time maxTime is a calibratable time period corresponding to a desired time window for prediction. 
     If the determination of operation  112  is negative, i.e. t_cp is less than maxTime, then for all obstacles in the reduced list, a predicted obstacle position is calculated at time t_cp, as illustrated at block  114 . For example, when t_cp is equal to zero, the predicted obstacle position may be equal to the obstacle position obtained from the sensor fusion output. When t_cp is greater than zero, the predicted obstacle position may be predicted based on positions and relative velocities of the host vehicle and the respective obstacle in the reduced list. 
     Predicted relative distances between the vehicle on the predicted path and the predicted location of the obstacles, calculated in block  114 , are then calculated, as illustrated in block  116 . 
     A determination is made of whether, for all obstacles in the reduced list, the predicted relative distance calculated at block  116  is greater than a second evaluation distance minDist2, as illustrated at operation  118 . The evaluation distance minDist2 is a calibratable parameter corresponding to a range of possible locations of the host vehicle and detected obstacles at time t_cp, based on a confidence level in the predicted path and predicted locations of the obstacles. In an exemplary embodiment, minDist2 is calibrated to increase as t_cp increases, along with t_pp discussed below. Thus, for shorter-term predictions a smaller range is evaluated, while for longer-term predictions a larger range is evaluated. 
     If the determination of operation  118  is positive, i.e. the predicted relative distance for all obstacles in the reduced list exceeds minDist2, then t_cp is incremented by a calibratable time increment dt, as illustrated at block  120 . Control then returns to operation  112 . 
     Returning to operation  112 , if the determination of operation  112  is positive, i.e. t_cp is not less than maxTime, then an obstacle_avoid_verify flag is set to ACCEPT, as illustrated at block  122 . Setting the obstacle_avoid_verify flag to ACCEPT indicates that the obstacle avoidance verification algorithm has determined that the predicted path based on the path planning output would not result in the vehicle contacting any detected obstacles within the time interval maxTime. In response to the obstacle_avoid_verify flag being set to ACCEPT, the orthogonal copilot system  52  may command the actuators  50  to accept the vehicle control output  48 . 
     Returning to operation  118 , if the determination of operation  118  is negative, i.e. the predicted relative distance for at least one obstacle in the reduced list does not exceed minDist2, then control proceeds to block  126 . 
     A second time counter t_pp is initialized to zero, as illustrated at block  126 . As will be discussed in further detail below, the second time counter t_pp corresponds to a temporal window for prediction of vehicle and obstacle locations relative to a predicted vehicle path based on current actuator settings. 
     A determination is made of whether t_pp is greater than or equal to the maximum evaluation time maxTime, as illustrated at operation  128 . As discussed above, the maximum evaluation time maxTime is a calibratable time period corresponding to a desired time window for prediction. 
     If the determination of operation  128  is negative, i.e. t_pp is less than maxTime, then for all obstacles in the reduced list, a predicted obstacle position is calculated at time t_pp, as illustrated at block  130 . For example, when t_pp is equal to zero, the predicted obstacle position may be equal to the obstacle position obtained from the sensor fusion output. When t_pp is greater than zero, the predicted obstacle position may be predicted based on positions and relative velocities of the host vehicle and the respective obstacle in the reduced list. 
     Predicted relative distances between the vehicle on the predicted path and the predicted location of the obstacles, calculated in block  130 , are then calculated, as illustrated in block  132 . 
     A determination is made of whether, for all obstacles in the reduced list, the predicted relative distance calculated at block  132  is greater than the second evaluation distance minDist2, as illustrated at operation  134 . As discussed above, the evaluation distance minDist2 is a calibratable parameter corresponding to a range of possible locations, based on a confidence level in the predicted path and predicted locations of the obstacles. As discussed above, in an exemplary embodiment, minDist2 is calibrated to increase as t_pp increases. 
     If the determination of operation  134  is positive, i.e. the predicted relative distance for all obstacles in the reduced list exceeds minDist2, then t_pp is incremented by the calibratable time increment dt, as illustrated at block  136 . Control then returns to operation  128 . 
     Returning to operation  128 , if the determination of operation  128  is positive, i.e. t_pp is not less than maxTime, then the obstacle_avoid_verify flag is set to LIMIT, as illustrated at block  138 . Setting the obstacle_avoid_verify flag to LIMIT indicates that the obstacle avoidance verification algorithm has determined that the predicted path based on current actuator settings would not result in any detected obstacle passing within the threshold distance minDist2 of the vehicle. In response to the obstacle_avoid_verify flag being set to LIMIT, the orthogonal copilot system  52  may command the actuators  50  to modify the vehicle control output  48  to maintain current actuator settings. In an alternative embodiment, the orthogonal copilot system  52  may command the actuators  50  to modify the vehicle control output  48  to an intermediate value between the current actuator settings and the vehicle control output  48 . 
     Returning to operation  134 , if the determination of operation  134  is negative, i.e. the predicted relative distance for at least one obstacle in the reduced list does not exceed minDist2, then the obstacle_avoid_verify flag is set to REJECT, as illustrated at block  140 . Setting the obstacle_avoid_verify flag to REJECT indicates that the obstacle avoidance verification algorithm has determined that both the predicted path based on current actuator settings and the predicted path based on the path planning output would result in a detected obstacle passing within the threshold distance minDist2 of the vehicle. In response to the obstacle_avoid_verify flag being set to REJECT, the orthogonal copilot system  52  may command the actuators  50  to reject the vehicle control output  48  and to instead perform an alternative maneuver. The alternative maneuver may include, for example, a fallback command to safely stop the vehicle. Such maneuvers may be referred to as minimal risk condition maneuvers. 
     As may be seen, embodiments according to the present disclosure may enable independent validation of autonomous vehicle control commands to aid in diagnosis of software or hardware conditions in the primary control system. Embodiments according to the present disclosure may thus be more robust, increasing customer satisfaction. 
     The processes, methods, or algorithms disclosed herein can be deliverable to/implemented by a processing device, controller, or computer, which can include any existing programmable electronic control unit or dedicated electronic control unit. Similarly, the processes, methods, or algorithms can be stored as data and instructions executable by a controller or computer in many forms including, but not limited to, information permanently stored on non-writable storage media such as ROM devices and information alterably stored on writeable storage media such as floppy disks, magnetic tapes, CDs, RAM devices, and other magnetic and optical media. The processes, methods, or algorithms can also be implemented in a software executable object. Alternatively, the processes, methods, or algorithms can be embodied in whole or in part using suitable hardware components, such as Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), state machines, controllers or other hardware components or devices, or a combination of hardware, software and firmware components. Such example devices may be on-board as part of a vehicle computing system or be located off-board and conduct remote communication with devices on one or more vehicles. 
     As previously described, the features of various embodiments can be combined to form further embodiments of the invention that may not be explicitly described or illustrated. While various embodiments could have been described as providing advantages or being preferred over other embodiments or prior art implementations with respect to one or more desired characteristics, those of ordinary skill in the art recognize that one or more features or characteristics can be compromised to achieve desired overall system attributes, which depend on the specific application and implementation. These attributes can include, but are not limited to cost, strength, durability, life cycle cost, marketability, appearance, packaging, size, serviceability, weight, manufacturability, ease of assembly, etc. As such, embodiments described as less desirable than other embodiments or prior art implementations with respect to one or more characteristics are not outside the scope of the disclosure and can be desirable for particular applications. 
     While exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms encompassed by the claims. The words used in the specification are words of description rather than limitation, and it is understood that various changes can be made without departing from the spirit and scope of the disclosure. As previously described, the features of various embodiments can be combined to form further embodiments of the invention that may not be explicitly described or illustrated. While various embodiments could have been described as providing advantages or being preferred over other embodiments or prior art implementations with respect to one or more desired characteristics, those of ordinary skill in the art recognize that one or more features or characteristics can be compromised to achieve desired overall system attributes, which depend on the specific application and implementation. These attributes can include, but are not limited to cost, strength, durability, life cycle cost, marketability, appearance, packaging, size, serviceability, weight, manufacturability, ease of assembly, etc. As such, embodiments described as less desirable than other embodiments or prior art implementations with respect to one or more characteristics are not outside the scope of the disclosure and can be desirable for particular applications.