Patent Publication Number: US-2023144600-A1

Title: Detection and Localization of Non-Line-of-Sight Objects Using Multipath Radar Reflections and Map Data

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of U.S. Provisional Patent Application No. 63/276,180, filed Nov. 5, 2021, which is incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     Many vehicles use vision-based systems or radar systems to detect and track objects (e.g., other vehicles) for assisted-driving systems and autonomous-driving systems. Vision-based systems, however, generally cannot detect objects located outside their direct line of sight. Radar systems generally only track line-of-sight objects. In many automotive environments (e.g., windy roads, blind curves, blind crests, urban intersections), detection of moving non-line-of-sight (NLOS) objects would improve object tracking and allow for early detection to improve safety. 
     SUMMARY 
     This document describes techniques and systems to detect and localize and NLOS objects using multipath radar reflections and map data. In some examples, a radar system for installation on a vehicle includes a transmitter, a receiver, and a processor. The processor can identify a detection of an object using reflected EM energy and determine, using map data, whether a direct-path reflection associated with the detection is within a roadway corresponding to the vehicle&#39;s travel path. In response to determining that the direct-path reflection is not located within the roadway, the processor can determine whether a multipath reflection (e.g., a multipath range and multipath angle) associated with the detection is viable. The viability of the multipath reflection can be based on the presence of reflective surfaces in the vicinity of the vehicle. In response to determining that the multipath reflection is viable, the processor can determine that the detection corresponds to an NLOS object and, in some implementations, start a track for the NLOS object. The processor can also indicate the NLOS object as an input to an autonomous or semi-autonomous driving system of the vehicle. In this way, the described techniques and systems can enable a radar system to detect and localize NLOS objects, thereby improving the safety of autonomous and semi-autonomous driving systems. 
     This document also describes methods performed by the above-summarized system and other configurations of the radar system set forth herein and means for performing these methods. 
     This Summary introduces simplified concepts related to detecting and localizing NLOS objects using multipath radar reflections and map data that are further described in the Detailed Description and Drawings. This Summary is not intended to identify essential features of the claimed subject matter, nor is it intended for use in determining the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The details of one or more aspects of techniques and systems to detect and localize NLOS objects using multipath radar reflections and map data are described in this document with reference to the following figures. The same numbers are often used throughout the drawings to reference like features and components: 
         FIG.  1    illustrates an example environment in which a radar system can detect and localize NLOS objects using multipath radar reflections and map data in accordance with techniques of this disclosure; 
         FIG.  2    illustrates a schematic representation of multipath radar propagation and illustrates key concepts for using multipath radar reflections to detect and localize NLOS objects; 
         FIG.  3    illustrates an example configuration of a vehicle with a radar system that can detect and localize NLOS objects using multipath radar reflections and map data; 
         FIG.  4    illustrates an example method of a radar system to detect and localize NLOS objects using multipath radar reflections and map data; 
         FIG.  5    illustrates an example flowchart of the described techniques and systems to detect and localize NLOS objects using multipath radar reflections and map data; and 
         FIG.  6 - 1  through  6 - 4    illustrate example detections and localizations of an NLOS object using multipath radar reflections and map data in accordance with techniques of this disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     Many vehicles use vision-based systems and/or radar systems to detect and track objects (e.g., other vehicles). For example, a camera can capture images of a vehicle&#39;s environment and process the image data to identify objects. Vision-based systems, however, generally cannot detect objects located outside their direct line of sight. As a result, these vision-based systems are unable to detect NLOS objects in many automotive environments, including windy roads, blind curves, blind crests, and urban intersections. 
     Radar systems use antennas to transmit and receive electromagnetic (EM) signals for detecting and tracking objects. In automotive applications, radar systems operate in dynamic environments that can cause EM signals to have multipath reflections. Multipath reflections occur when a reflective surfaces (e.g., a wall, fence, barrier, guardrail, sign, or another vehicle) creates one or more additional reflections to an EM signal. The multipath environment can result in both direct-path reflections and multipath reflections. A direct-path reflection occurs when a reflected EM signal travels directly between the radar system and the object. Typically, multipath reflections occur when the received EM signals reflect off one or more objects and take multiple paths to travel between the object and the radar system. In this document, it is understood that multipath reflections can also describe a single, indirect path from an object. Multipath reflections, however, create mirror images (e.g., echoes) of the detected object. Radar systems generally do not have a simple way to differentiate between an object&#39;s direct return and its multipath echo. As a result, due to their outlier appearance, NLOS objects are typically perceived as noise and negatively impact tracking systems. As a result, radar systems generally filter out multipath reflections and, thus, are unable to detect NLOS objects. 
     In contrast, this document describes techniques and systems to detect and localize NLOS objects using multipath radar reflections and map data. Taking advantage of multipath reflections, radar systems can be configured as an important sensing technology that vehicle-based systems use to acquire information about the surrounding environment, including NLOS objects. Vehicle-based systems can use radar systems to detect objects around a blind corner, vehicles on the opposide side of a crest and, if necessary, take necessary actions (e.g., stop, reduce speed, change lanes) to avoid a collision. 
     For example, a radar system can include a transmitter to transmit EM energy and a receiver to receive EM energy reflected by objects in a vicinity of a vehicle. The radar system also includes one or more processors to perform the NLOS-object detection and localization. Using the received EM energy, the processor can identify a detection of an object in the vehicle&#39;s vicinity. The processor can determine, using map data, whether a direct-path reflection (e.g., a direct-path angle and direct-path range) associated with the detection is located within the roadway. Responsive to determining that the direct-path reflection associated with the detection is not located within the roadway, the processor can determine whether a multipath reflection (e.g., a multipath range and multipath angle) associated with the detection is viable. Viability can include, but not be limited to, a reflective surface identified in the environment by sensors or in map data as well as registering the multipath reflection and multipath-tracked NLOS object on a map to determine if it is located in a lane beyond sensor range. Responsive to determining that the multipath reflection is viable, the processor can determine that the detection corresponds to an NLOS object and indicate the NLOS object as an input to an autonomous or semi-autonomous driving system. In this way, the described systems and techniques can provide NLOS detection and localization without the added cost of high-precision lidar systems. The NLOS detection and localization can also be used to improve the safety of assisted-driving or autonomous-driving systems. 
     This example is just one example of the described techniques and systems to detect and localize NLOS objects using multipath radar reflections and map data. This document describes other examples and implementations. 
     Operating Environment 
       FIG.  1    illustrates an example environment  100  in which a radar system  104  can detect and localize NLOS objects using multipath radar reflections and map data in accordance with the techniques of this disclosure. In the depicted environment  100 , the radar system  104  is mounted to, or integrated within, a vehicle  102  traveling on a road  124 . The radar system  104  can detect one or more objects in the vicinity of the vehicle  102 , including an NLOS object  122  which is not within the line-of-sight of the radar system  104 . In the depicted environment, the road  124  includes a blind corner obstructing the view of the radar system  104 . 
     Although illustrated as a passenger truck, the vehicle  102  can represent other types of motorized vehicles (e.g., a car, an automobile, a motorcycle, a bus, a tractor, a semi-trailer truck), non-motorized vehicles (e.g., a bicycle), railed vehicles (e.g., a train), watercraft (e.g., a boat), or aircraft (e.g., an airplane). In general, manufacturers can mount the radar system  104  to any moving platform, including moving machinery or robotic equipment. 
     In the depicted implementation, the radar system  104  is mounted on the front of the vehicle  102  and illuminates the object  122  via a reflection off of a reflective surface  120  (e.g., a guardrail). The radar system  104  can detect the NLOS object  122  from any exterior surface of the vehicle  102 . For example, vehicle manufacturers can integrate the radar system  104  into a bumper, side mirror, headlights, rear lights, or any other interior or exterior location where objects require detection. In some cases, the vehicle  102  includes multiple radar systems  104 , such as a first radar system  104  and a second radar system  104 , that provide a larger instrument field-of-view. In general, vehicle manufacturers can design the locations of the radar systems  104  to provide a particular field-of-view that encompasses a region of interest. Example fields-of-view include a 360-degree field-of-view, one or more 180-degree fields-of-view, one or more 90-degree fields-of-view, and so forth, which can overlap or be combined into a field-of-view of a particular size. 
     The NLOS object  122  and the reflective surface  120  are composed of one or more materials that reflect radar signals. Depending on the application, the NLOS object  122  can represent a target of interest. In some cases, the NLOS object  122  and the reflective surface  120  can be moving objects (e.g., other vehicles) or stationary objects (e.g., roadside signs, road barriers, debris). Depending on the application, the NLOS object  122  can represent a target of interest from which the vehicle  102  can safely navigate the road  124 . 
     The radar system  104  emits EM radiation by transmitting EM signals  118  or waveforms via antenna elements. In the environment  100 , the radar system  104  can detect and track the NLOS object  122  by transmitting and receiving one or more radar signals. For example, the radar system  104  can transmit EM signals between one hundred and four hundred gigahertz (GHz), between four and one hundred GHz, or between approximately seventy and eighty GHz. 
     The radar system  104  can be a MIMO radar system that relies on uniform linear arrays (ULAs) to match the reflected EM signals to corresponding objects. The radar system  104  can also operate as a traditional radar system that does not rely on MIMO techniques. The radar system  104  can include a transmitter  106  to transmit the EM signals  118 . The radar system  104  can also include a receiver  108  to receive reflected versions of the EM signals  118 . The transmitter  106  includes one or more components, including an antenna or antenna elements, for emitting the EM signals  118 . The receiver  108  includes one or more components, including an antenna or antenna elements, for detecting the reflected EM signals. The transmitter  106  and the receiver  108  can be incorporated together on the same integrated circuit (e.g., a transceiver integrated circuit) or separately on different integrated circuits. In other implementations, the radar system  104  does not include a separate antenna, but the transmitter  106  and the receiver  108  each include one or more antenna elements. 
     The radar system  104  also includes one or more processors  110  (e.g., an energy processing unit or electronic control unit) and computer-readable storage media (CRM)  112 . The processor  110  can be a microprocessor or a system-on-chip. The processor  110  can execute instructions stored in the CRM  112 . For example, the processor  110  can process EM energy received by the receiver  108  and determine, using an NLOS detection component  114  and map data  116 , detections and localization data associated with the NLOS object  122 . The processor  110  can also registor the NLOS object  122  to a roadway infrastructure using the map data  116 . The processor  110  can also detect various features (e.g., range, azimuth angle, range rate, elevation) of the NLOS object  122 . The processor  110  can generate radar data, including the position and velocity of the NLOS object  122 , for at least one automotive system. For example, the processor  110  can control, based on processed EM energy from the receiver  108 , an autonomous or semi-autonomous driving system of the vehicle  102 . 
     The map data  116  can provide a map of travel routes (e.g., highways, freeways, streets, roads) in an area along with objects near the travel routes (e.g., road barriers, traffic signs, guardrails). The map data  116  can be stored in CRM  112 . In other implementations, the map data  116  can be stored in an online database or on a remote computing device, and the processor  110  can download, via communication devices, the map data  116 . As described in greater detail below, the NLOS detection component  114  can use map data  116  to determine the viability of potential direct-path and multipath reflections associated with detections. 
     The NLOS detection component  114  obtains EM energy received by the receiver  108  or detections from an intermediate component. The NLOS detection component  114  uses the received EM energy or detections to identify detections associated with the NLOS object  122  and localize the NLOS object  122  relative to the radar system  104 . As described above, multipath reflections are a propagation phenomenon where return radar signals travel via indirect paths. As a result of a multipath reflection, mirror images or echoes of the target (e.g., the NLOS object  122 ) are present in the radar data. The NLOS detection component  114  can use the techniques described below with respect to  FIGS.  2  through  5    to differentiate between potential direct-path returns and multipath reflections associated with detections. The NLOS detection component  114  can also generate a track for the detected NLOS objects to follow their position and velocity. The radar system  104  can implement the NLOS detection component  114  as instructions in the CRM  112 , hardware, software, or a combination thereof executed by the processor  110 . 
     The radar system  104  can determine a distance to the NLOS object  122  based on the time it takes for the EM signals to travel from the radar system  104  to the NLOS object  122 , and from the NLOS object  122  back to the radar system  104 . The radar system  104  can also determine, using the NLOS detection component  114 , a location of the NLOS object  122  in terms of a direction of departure (DoD) and a direction of arrival (DoA) based on the direction of one or more large-amplitude echo signal received by the radar system  104 . 
     As an example,  FIG.  1    illustrates the vehicle  102  traveling on a road  124  that includes a blind corner. A reflective surface  120  is near the road  124 . The reflective surface  120  can be a wall, guardrail, fence, building, or another vehicle. The radar system  104  can detect the NLOS object  122  in front of the vehicle  102  using the described techniques of this disclosure. 
     The transmitter  106  of the radar system  104  transmits the EM signal  118  in front of the vehicle  102 . The EM signal  118  reflects off of the reflective surface  120  toward the NLOS object  122 , which can be located outside the field-of-view of the radar system  104 . The NLOS object  122  reflects the transmitted EM signal  118 . The reflected EM signal reflects off of the reflective surface  120  and travels back to the receiver  108 . The NLOS detection component  114  detects and localizes the NLOS object  122 . For example, the radar system  104  can localize the NLOS object  122  in terms of a vehicle coordinate system with an x-axis (e.g., in a forward direction along the road  124 ) and a y-axis (e.g., perpendicular to the x-axis and along a surface of the road  124 ). The NLOS detection component  114  can also determine, using the map data  116 , that a direct-path reflection associated with the detection is not located within the road  124  (e.g., conceivably positioned far from the vehicle and off the sheet that includes  FIG.  1   ). 
     The vehicle  102  can also include at least one automotive system that relies on data from the radar system  104 , such as a driver-assistance system, an autonomous-driving system, or a semi-autonomous-driving system. The radar system  104  can include an interface to the automotive systems that rely on the data. For example, the processor  110  outputs, via the interface, a signal based on EM energy received by the receiver  108 . 
     Generally, the automotive systems use radar data provided by the radar system  104  to perform a function. For example, the driver-assistance system can provide NLOS monitoring and generate an alert that indicates a potential collision with the NLOS object  122  (e.g., the NLOS object  122  is in the same lane as the vehicle  102  and is traveling at a slower speed). The radar data can also indicate when it is safe or unsafe to maintain a current lane or speed. The autonomous-driving system may move the vehicle  102  to a particular location on the road  124  while avoiding collisions with the NLOS object  122 . The radar data provided by the radar system  104  can provide information about the distance to and the location of the NLOS object  122  to enable the autonomous-driving system to perform emergency braking, perform a lane change, lane bias, or adjust the speed of the vehicle  102 . 
       FIG.  2    illustrates a schematic representation  200  of multipath radar propagation and illustrates key concepts for using multipath radar reflections to detect and localize NLOS objects. An object  20  can be at different positions relative to vehicle  102 , including being within or outside of the line-of-sight of the radar system  104 . The reflective surface  120  (e.g., a guardrail, a railing, a construction zone boundary, a fence, another vehicle) or a reflective object is positioned to the right of vehicle  102 . The radar system  104  detects the object  202 , by emitting the EM signal  118  and receiving a reflected EM signal. 
     The radar system  104  transmits the EM signal  118  in different directions and when reflected some of these signals can reach the receiver  108  via multiple paths. The shortest path is a direct-path transmission and reflection of the radar signal, as represented by a direct-path signal  204  in  FIG.  2   . The direct-path signal  204  generally also results in the strongest (e.g., highest energy level) reflected signal. However, the direct-path signal  204  is not always available due to obstructions, curves in the roadway, or other issues. As a result, the object  202  can be outside of the line-of-sight for the radar system  104 . 
     The reflected signal can also propagate to the receiver  108  via one or more indirect paths. Indirect paths can occur when the reflective surface  120  redirects the EM signals. For example, the EM signal  118  can bounce off of the reflective surface  120  before reflecting off of the object  202  and travel back to the receiver  108  via the same path, resulting in a multipath reflection. 
     Multipath reflections allow the radar system  104  to detect NLOS objects via indirect paths. As a result of the multipath reflection, the radar system  104  must determine whether the detection is the result of a direct-path return or a multipath return. The direct-path return is associated with an image  206  of the object  202 . The image  206  results from assuming a direct-path return based on the direction-of-arrival for the EM signal received by the receiver  108 . As illustrated in  FIG.  2   , the range for the multipath return and the direct-path return is similar, but the NLOS detection component  114  can use the map data  116  to differentiate between a multipath return and a direct-path return as described in greater detail below concerning  FIGS.  4  and  5   . 
     Multipath reflections can generally occur via specular reflections or diffuse reflections. A specular reflection assumes a flat and smooth mirroring surface, whereas a diffuse reflection assumes a rough surface causing many returns. In the automotive environment, the radar system  104  can generally assume that the reflective surface  120  is approximately specular. 
     The multipath reflection for object  202  via the reflective surface  120  can be defined as a sum of direct and indirect paths, which is illustrated in Equation (1): 
         s   r ( t )= s   t ( t−R   d   /C )+ Gs   t ( t−R   i   /C ),  (1)
 
     where s t  is the transmitted signal  118 , R d  and R i  are direct and indirect path ranges, respectively, and G is the earth&#39;s reflection coefficient. The received EM signal can be further written as shown in Equation (2): 
     
       
         
           
             
               
                 
                   
                     
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     where ΔR=R i −R d . The described radar system  104  and NLOS detection component  114  can perform object detection and localization for object  202  as described in greater detail below concerning  FIGS.  3  through  5   . 
     Vehicle Configuration 
       FIG.  3    illustrates an example configuration of a vehicle with a radar system that can detect and localize NLOS objects using multipath radar reflections and map data. As described for  FIG.  1   , the vehicle  102  can include the radar system  104 , the processor  110 , the CRM  112 , the NLOS detection component  114 , which includes an NLOS localization component  308  and an NLOS track component  310 , and map data  116 . In addition, the radar system includes a range and range-rate component  304  and an angle-finding component  306 . The vehicle  102  can also include one or more communication devices  302  and a control interface  312  to one or more vehicle-based systems, including one or more assisted-driving systems  314  and one or more autonomous-driving systems  316 . 
     The communication devices  302  can include a sensor interface and a vehicle-based system interface. The sensor interface and the vehicle-based system interface can transmit data (e.g., radar data, range computations, tracks, and other features mapped to the NLOS objects  122 ) over a communication bus of the vehicle  102 , for example, when the individual components of the radar system  104  and/or the NLOS detection component  114  are integrated within the vehicle  102 . 
     The vehicle  102  also includes the control interface  312  to one or more vehicle-based systems (e.g., the assisted-driving systems  314  and autonomous-driving systems  316 ), which individually or in combination provide a way for receiving radar data to control the vehicle  102 . Some examples of vehicle-based systems to which the control interface  312  supplies radar data include the assisted-driving system  314  and the autonomous-driving system  316 ; each may rely on information output from the NLOS detection component  114 . For example, the vehicle-based systems may rely on data, which is communicated via the communication devices  302  and obtained from the radar system  104 , to operate the vehicle  102  (e.g., braking, lane changing). Generally, the control interface  312  can use data provided by the NLOS detection component  114  to control operations of the vehicle  102  and perform certain functions not requiring control but also for outputting warnings to passengers, pedestrians, and other vehicles. For example, the assisted-driving system  314  can alert a driver of the NLOS objects  122  and perform evasive maneuvers to avoid a collision with the NLOS object  122 . As another example, the autonomous-driving system  316  can navigate the vehicle  102  to a particular position in the road  124  to avoid a collision with the NLOS object  122 . The vehicle-based systems can also provide early warnings or alerts to a driver about the NLOS object  122  (e.g., if the NLOS object  122  is located in the same lane as the vehicle  102  and presents an unsafe condition). 
     The range and range-rate component  304  receives radar data as input and then outputs processed radar data, which can include a range, a range rate, and classification of the NLOS objects  122 . An input-processing function of the range and range-rate component  304  may maintain a feature-extraction function, as well as a post-processing or output function. The input-processing function enables the range and range-rate component  304  to receive radar data from the transmitter  106  and/or receiver  108  (e.g., to build a synthetic array). 
     In some implementations, the radar data is received as low-level, time-series data obtained from a MIMO antenna array to generate a synthetic array, which maps radar returns (e.g., narrowband signals) to input and output channels of the synthetic array. Using low-level, time-series data enables the NLOS detection component  114  to provide better detection resolution with the range and range-rate component  304  to extract features associated with the NLOS objects  122  that appear in the environment  100 . The synthetic array can be formed using MIMO techniques to map the narrowband signals obtained to input and output channels of the synthetic array by impinging on the synthetic array from distinct directions. 
     The angle-finding component  306  obtains EM energy received by the receiver  108  and determines angles associated with the NLOS objects  122 . For example, the angle-finding component  306  can process the radar data to generate range-angle maps or interpolated range-angle maps, including range-azimuth maps and/or range-elevation maps. The interpolated range-azimuth format can improve the accuracy of the NLOS detection component  114  by simplifying the labeling of NLOS objects  122 , e.g., for use by a machine-learned model that is configured to make further estimations or predictions from the radar data received as input. The radar system  104  can implement the angle-finding component  306  as instructions in the CRM  112 , hardware, software, or a combination thereof executed by the processor  110 . 
     As described above, the NLOS detection component  114  can detect and localize the NLOS objects  122 . In particular, the NLOS localization component  308  can determine a range and azimuth angle associated with the NLOS objects  122 . The NLOS localization component  308  can determine a multipath range and a multipath angle associated with the NLOS objects  122 . 
     For example, the NLOS localization component  308  can use a direct-path angle associated with an NLOS object  122  and determine whether a reflective object or reflective surface is located along the direct-path angle associated with the detection of the NLOS object  122 . Upon determining that a reflective object or a reflective surface is located along the direct-path angle associated with the detection, the NLOS localization component  308  can determine an angle of reflection for the received EM energy at the reflective object or reflective surface. The angle of reflection can be determined based on the direct-path angle associated with the detection. The angle of reflection can also be determined based on the map data  116  or other sensor data (e.g., radar data, vision data, or lidar data) indicating an angle of the reflective surface relative to the radar system  104 . The NLOS localization component  308  can also determine an angle of incidence for the received EM energy at the reflective object or the reflective surface based on the angle of reflection. The NLOS localization component  308  can then determine the multipath range and the multipath angle associated with the detection or the NLOS object  122  based on the angle of incidence and the direct-path range, respectively. 
     The NLOS track component  310  can generate tracks for each detected NLOS object  122  and append data to the NLOS tracks as additional localization information is determined for the NLOS objects  122 . In this way, the radar system  104  can detect and localize the NLOS objects  122  earlier than other radar systems without the added cost or processing complexity of high-precision lidar systems. In addition, the described techniques and systems can improve the safety of the assisted-driving systems  314  and the autonomous-driving systems  316  by providing detection and localization data for the NLOS objects  122 . 
     Example Method 
       FIG.  4    illustrates an example method  400  of a radar system to detect and localize NLOS objects using multipath radar reflections and map data. Method  400  is shown as sets of operations (or acts) performed, but not necessarily limited to the order or combinations in which the operations are shown herein. Further, any of one or more of the operations may be repeated, combined, or reorganized to provide other methods. In portions of the following discussion, reference may be made to the environment  100  of  FIG.  1   , and entities detailed in  FIGS.  1  through  3   , reference to which is made for example only. The techniques are not limited to performance by one entity or multiple entities. 
     At  402 , a detection of an object is identified using received EM energy. For example, the transmitter  106  of the radar system  104  can transmit EM energy, including the EM signal  118 . The receiver  108  of the radar system  104  can receive EM energy reflected by one or more objects, including the NLOS object  122 . The processor  110  of the radar system  104  can identify a detection of the NLOS object  122  using the received EM energy. The NLOS object  122  can be a stationary object or a moving object. The radar system  104  can use Doppler data to filter out NLOS objects  122  that are stationary. 
     At  404 , it is determined whether a direct-path reflection associated with the detection is located within a roadway corresponding to a travel path of a vehicle using map data. For example, the processor  110  can determine, using map data, whether a direct-path angle and a direct-path range associated with the detection is located within the road  124 . In particular, the processor  110  can determine, using the received EM energy, an angle (e.g., an azimuth angle) associated with the detection. The processor  110  can also determine, using the EM energy, a direct-path range associated with the detection. The map data can be included in CRM  112  or obtained over a communication link with a remote database. 
     At  406  and responsive to a determination that the direct-path reflection associated with the detection is not located within the roadway, it is determined whether a multipath reflection (e.g., a multipath range and multipath angle) associated with the detection is viable. For example, in response to determining that the direct-path reflection (e.g., a direct-path angle and a direct-path range) associated with the detection is not located within the road  124 , the processor  110  can determine whether a multipath range and multipath angle associated with the detection are viable. The viability determination can be based on potential reflections of the EM energy off of objects (e.g., guardrails, signs, road barriers) included in the map data  116  or nearby objects (e.g., another vehicle, guardrails, signs, billboard) detected by one or more vehicle sensors (e.g., a vision system, a camera system, a lidar system, or the radar system  104 ). The viability determination can also be based on a determination whether the multipath range and multipath angle associated with the detection are registerable on the roadway infrastructure or geometry included in the map data  116 . 
     The processor  110  can determine, using the map data  116  or data from the radar system  104 , a lidar system, or a vision system, whether a reflective object is located along the direct-path angle associated with the detection. The viability of the reflective object that generates the multipath reflection can be enhanced by the location of the reflective object being included in an a priori map or a current sensor map. Responsive to a determination that a reflective object is located along the direct-path angle associated with the detection, the processor  110  can determine an angle of reflection for the received EM energy at the reflective object. The angle of reflection can be determined based on the direct-path angle associated with the detection. For example, the processor  110  can use the map data or sensor data to determine an angle of the reflective object relative to the radar system  104  and then determine the angle of reflection based on the direct-path angle and the relative angle. The processor  110  can then determine, based on the angle of reflection for the received EM energy at the reflective object, an angle of incidence for the received EM energy at the reflective object. Using the angle of incidence for the received EM energy at the reflective object and the direct-path range associated with the detection, the processor  110  can determine the multipath range and multipath angle associated with the detection. The processor  110  can then determine, using the map data  116 , whether the multipath range and the multipath angle associated with the detection is viable. 
     At  408 , the detection is determined to correspond to an NLOS object responsive to a determination that the multipath reflection associated with the detection is viable. For example, responsive to a determination that the multipath range and the multipath angle associated with the detection are viable, the processor  110  can determine that the detection corresponds to the NLOS object  122 . In making this determination, the processor  110  can also determine, using road geometries included in the map data  116 , whether a multipath position associated with the detection is viable. The processor  110  can also start or update a track associated with the NLOS object  122  with the multipath range, multipath angle, multipath range rate, or multipath position associated with the detection. The processor  110  can use registration to the map data  116  to maintain viability of the tracked NLOS object  122 . 
     At  410 , an indication of the NLOS object is provided as an input to an autonomous-driving or assisted-driving system. For example, the radar system  104  can provide an indication of the NLOS object  122  as an input to the assisted-driving system  314  or the autonomous-driving system  316 . The NLOS data can include the multipath range, multipath angle, multipath position, multipath range rate, or track associated with the NLOS object  122 . In this way, the assisted-driving system  314  or the autonomous-driving system  316  can improve its safety. 
       FIG.  5    illustrates an example flowchart  500  of the described techniques and systems to detect and localize NLOS objects using multipath radar reflections and map data. The radar system of  FIG.  5    can, for example, be the radar system  104  of  FIGS.  1  and  3   , which includes the NLOS detection component  114  and the map data  116 . 
     At  502 , the NLOS detection component  114  determines whether a target (e.g., a moving object or a stationary object) is detected. In particular, the transmitter  106  of the radar system  104  can transmit EM energy, including the EM signal  118 . The receiver  108  of the radar system  104  can receive EM energy reflected by one or more objects, including the NLOS object  122 . The processor  110  of the radar system  104  can identify a detection of the NLOS object  122  using the received EM energy. The processor  110  can also determine whether the detection indicates the NLOS object  122  is moving or stationary. 
     At  504 , if the detection is a target, the NLOS detection component  114  determines whether an angle and range associated with the detection are located on the mapped roadway. In particular, the processor  110  can determine, using the map data  116  and assuming a direct-path reflection, whether an angle (e.g., an azimuth angle) and range associated with the detection is located within the road  124 . As another example, the processor  110  can determine that a reflective surface is located along the direct-path angle of the detection. If the map data  116  indicates that it is improbable for a moving object to be located on the other side of the reflective surface, then the NLOS detection component  114  can determine with a higher level of certainty that the detection is the result of a multipath reflection from an NLOS object. As another example, the processor  110  can determine that the multipath reflection from the NLOS object  122  is on the road  124  by using the map data  116 . 
     At  506 , if the direct-path angle and direct-path range associated with the detection are not located on the mapped roadway, the NLOS detection component  114  determines whether a multipath range and a multipath angle associated with the detection are viable based on nearby objects. In particular, the viability determination can be based on potential reflections of the EM energy off of objects (e.g., guardrails, signs, road barriers) included in the map data  116  or nearby objects (e.g., another vehicle, guardrails, signs, billboard) detected by one or more vehicle sensors (e.g., a vision system, a camera system, lidar system, or the radar system  104 ). As described above, the NLOS detection component  114  can use the direct-path angle, the direct-path range, and a relative angle of a reflective object to determine the multipath angle and multipath range associated with the detection. 
     At  508 , if the multipath range and multipath angle are viable, the NLOS detection component  114  determines whether a multipath position associated with the detection is viable based on the roadway geometry. In particular, the processor  110  can determine, using the the map data  116 , whether the multipath position is located on the road  124 . In particular, the processor  110  can determine, using the map data  116 , whether the multipath position (e.g. the multipath range and the multipath angle) is located within a lane of the road  124 . The processor  110  can also use the multipath position to determine in which lane of the road  124  the detection is located. 
     At  510 , if the multipath position is viable based on the roadway geometry, the NLOS detection component  114  determines that an NLOS object  122  is detected. In addition, if the map data  116  includes lane-level map data (e.g., indicating individual lanes of the road  124 ) or HD map data, then the NLOS detection component  114  can also use the multipath position of the NLOS object  122  to determine in which lane the NLOS object  122  is located and register the NLOS object  122  to a map to match the multipath position and trajectory to the road shape of the road  124 . 
     At  512 , the NLOS detection component  114  also starts, updates, or appends an NLOS track for the NLOS object  122  with the multipath range, multipath angle, and/or multipath position associated with the NLOS object  122 . 
     At  514 , the NLOS detection component  114  continues to the next detection and begins the flowchart  500  again if a negative response is determined for steps  502 ,  506 , or  508  or a positive response is determined for step  504 . 
     The described techniques and systems of the NLOS detection component  114  allow the radar system  104  to detect and localize NLOS objects using multipath radar reflections and map data. In this way, the described systems and techniques provide NLOS detections and localizations without the added cost of high-precision lidar systems. The NLOS detections and localizations also improve the safety and effectiveness of the assisted-driving system  314  and the autonomous-driving system  316 . 
       FIG.  6 - 1  through  6 - 4    illustrate example detections and localizations of an NLOS object using multipath radar reflections and map data in accordance with techniques of this disclosure. In the illustrated environment  600 - 1  through  600 - 4 , a vehicle (e.g, the vehicle  102 ) is traveling on a road with a curve to the right. The vehicle  102  includes a radar system (e.g., the radar system  104 ), a vision-based system (e.g., a camera), and map data (e.g., the map data  116 ). Vision images  602 - 1  through  602 - 4  illustrate example still images of the road captured by the vision-based system in front of the vehicle. Plots  604 - 1  through  604 - 4  illustrate example radar point clouds generated by the radar system  104 , providing the range (e.g., in meters) along a y-axis and the cross-range (e.g, in meters) along an x-axis associated with detections. 
     In  FIG.  6 - 1   , the vehicle  102  is approaching the curve in the road. The vision image  602 - 1  illustrates that a curve is approaching in about 100 meters. The plot  604 - 1  indicates detections  606  associated with guardrails, fencing, sign posts, utility poles, and other stationary objects along the side of the road. As illustrated in the vision image  602 - 1  and the plot  604 - 1  another vehicle is currently not detected along the road. 
     In  FIG.  6 - 2   , the vehicle  102  continues to approach the curve in the road. The vision image  602 - 2  and the plot  604 - 2  illustrate that the curve is now about 80 meters away. The plot  604 - 2  indicates the detections  606  associated with the guardrails, fencing, sign posts, utility poles, and other stationary objects along the side of the road. The plot  604 - 2  also indicates an NLOS detection  608  associated with a moving NLOS object (e.g., another vehicle) about 180 meters down range and about 45 meters to the right and around the corner. The NLOS object (e.g., the NLOS object  122 ) is not currently visible within the image  602 - 2 . As described with respect to  FIGS.  4  and  5   , the NLOS detection component  114  can determine that the NLOS detection  608  is due to a multipath reflection off of one of the stationary detections  606  (e.g., the guardrail to the left of the road). 
     In  FIG.  6 - 3   , the vehicle  102  continues to approach the curve in the road. The vision image  602 - 3  and the plot  604 - 3  illustrate that the curve is now about 50 meters away. The plot  604 - 3  indicates the detections  606  associated with the guardrails, fencing, sign posts, utility poles, and other stationary objects along the side of the road. The plot  604 - 3  also indicates the NLOS detection  608  associated with the moving NLOS object is now about 100 meters down range and about 35 meters to the right and around the corner. The NLOS object, however, is still not currently visible within the image  602 - 3 . 
     In  FIG.  6 - 4   , the vehicle  102  continues to approach the curve in the road. The vision image  602 - 4  and the plot  604 - 4  illustrate that the curve is now about 25 meters away. The plot  604 - 4  indicates the detections  606  associated with the guardrails, fencing, sign posts, utility poles, and other stationary objects along the side of the road. The plot  604 - 4  also indicates another vehicle  610 , which corresponds to the previous NLOS detections  608  in plots  604 - 2  and  604 - 3  and is now currently visible in the image  602 - 4 . The other vehicle  610  is now about 70 meters down range and about 15 meters to the right on the curve in the road. In the illustrated scenario of  FIGS.  6 - 1  through  6 - 4   , the radar system  104  was able to detect and localize the other vehicle  610  while it was still not within the field-of-view of the vision-based system and at least 70 meters before the vision-based system was able to detect it. 
     Examples 
     In the following section, examples are provided. 
     Example 1: A radar system comprising: a transmitter configured to transmit electromagnetic (EM) energy; a receiver configured to receive EM energy reflected by one or more objects; and one or more processors configured to: identify, using the received EM energy, a detection of an object; determine, using map data, whether a direct-path reflection associated with the detection is located within a roadway corresponding to a travel path of a vehicle; responsive to a determination that the direct-path reflection associated with the detection is not located within the roadway, determine whether a multipath reflection associated with the detection is viable; responsive to a determination that the multipath reflection associated with the detection is viable, determine that the detection corresponds to a non-line-of-sight (NLOS) object; and provide an indication of the NLOS object as an input to an autonomous-driving or assisted-driving system. 
     Example 2: The radar system of example 1, wherein the one or more processors are further configured to: determine, using the received EM energy, a direct-path angle and a direct-path range associated with the detection; and responsive to a determination that the multipath reflection associated with the detection is viable, determine, using the received EM energy and the map data, a multipath range and a multipath angle associated with the detection. 
     Example 3: The radar system of example 2, wherein the one or more processors are configured to determine whether the direct-path reflection associated with the detection is located within the roadway by determining, using the map data, whether the direct-path angle and the direct-path range associated with the detection are located within the roadway. 
     Example 4: The radar system of example 2 or 3, wherein the one or more processors are further configured to determine, using the map data, the multipath range, and the multipath angle, whether a multipath position associated with the detection is viable. 
     Example 5: The radar system of example 4, wherein a determination that the multipath position associated with the detection is viable comprises determining whether the multipath position is located within a lane of the roadway. 
     Example 6: The radar system of any of examples 2 through 5, wherein the one or more processors are configured to determine whether the multipath reflection associated with the detection is viable by: determining whether a reflective object is located along the direct-path angle associated with the detection; responsive to determining that a reflective object is located along the direct-path angle associated with the detection, determining, based on the direct-path angle associated with the detection, an angle of reflection for the received EM energy at the reflective object; determining, based on the angle of reflection for the received EM energy at the reflective object, an angle of incidence for the received EM energy at the reflective object; and determining the multipath range and multipath angle associated with the detection based on the angle of incidence for the received EM energy at the reflective object and the direct-path range associated with the detection, respectively. 
     Example 7: The radar system of example 6, wherein the one or more processors are configured to determine whether the reflective object is located along the direct-path angle associated with the detection based on reflective objects included in the map data or reflective objects detected by the radar system, a lidar system, or a vision system. 
     Example 8: The radar system of any preceding example, wherein: the NLOS object is moving; and the one or more processors are further configured to start or update a track associated with the NLOS object. 
     Example 9: The radar system of example 8, wherein the one or more processors are further configured to provide the track associated with the NLOS object as another input to the autonomous or semi-autonomous driving system. 
     Example 10: The radar system of any preceding example, wherein: the NLOS object is stationary; and the one or more processors are further configured to start or update a track associated with the NLOS object. 
     Example 11: The radar system of example 10, wherein the one or more processors are further configured to provide the track associated with the NLOS object as another input to the autonomous or semi-autonomous driving system. 
     Example 12: The radar system of any preceding example, wherein the radar system is configured to be installed in a vehicle. 
     Example 13: The radar system of example 12, wherein the map data is included in a memory of the vehicle. 
     Example 14: The radar system of example 12, wherein the map data is obtained from a remote computer system. 
     Example 15: A computer-readable storage media comprising computer-executable instructions that, when executed, cause a processor of a radar system to: identify, using EM energy reflected by one or more objects, a detection of an object; determine, using map data, whether a direct-path reflection associated with the detection is located within a roadway corresponding to a travel path of a vehicle; responsive to a determination that the direct-path reflection associated with the detection is not located within the roadway, determine whether a multipath reflection associated with the detection is viable; responsive to a determination that the multipath reflection associated with the detection is viable, determine that the detection corresponds to a non-line-of-sight (NLOS) object; and provide an indication of the NLOS object as an input to an autonomous-driving or assisted-driving system. 
     Example 16: The computer-readable storage media of example 15, wherein the computer-readable storage media further comprises computer-executable instructions that, when executed, cause the processor of the radar system to: determine, using the received EM energy, a direct-path angle and a direct-path range associated with the detection; and responsive to a determination that the multipath reflection associated with the detection is viable, determine, using the received EM energy and the map data, a multipath range and a multipath angle associated with the detection. 
     Example 17: The computer-readable storage media of example 16, wherein the computer-readable storage media further comprises computer-executable instructions that, when executed, cause the processor of the radar system to determine whether the direct-path reflection associated with the detection is located within the roadway by determining, using the map data, whether the direct-path angle and the direct-path range associated with the detection are located within the roadway. 
     Example 18: The computer-readable storage media of example 16 or 17, wherein the computer-readable storage media further comprises computer-executable instructions that, when executed, cause the processor of the radar system to determine, using the map data, the multipath range, and the multipath angle, whether a multipath position associated with the detection is viable. 
     Example 19: The computer-readable storage media of any of examples 16 through 18, wherein the computer-readable storage media further comprises computer-executable instructions that, when executed, cause the processor of the radar system to determine whether the multipath reflection associated with the detection is viable by: determining whether a reflective object is located along the direct-path angle associated with the detection; responsive to determining that a reflective object is located along the direct-path angle associated with the detection, determining, based on the direct-path angle associated with the detection, an angle of reflection for the received EM energy at the reflective object; determining, based on the angle of reflection for the received EM energy at the reflective object, an angle of incidence for the received EM energy at the reflective object; and determining the multipath range and multipath angle associated with the detection based on the angle of incidence for the received EM energy at the reflective object and the direct-path range associated with the detection, respectively. 
     Example 20: The computer-readable storage media of example 19, wherein the computer-readable storage media further comprises computer-executable instructions that, when executed, cause the processor of the radar system to determine whether the reflective object is located along the direct-path angle associated with the detection based on reflective objects included in the map data or reflective objects detected by the radar system, a lidar system, or a vision system. 
     Example 21: The computer-readable storage media of any of examples 15 through 20, wherein: the NLOS object is moving; and the computer-readable storage media further comprises computer-executable instructions that, when executed, cause the processor of the radar system to start or update a track associated with the NLOS object. 
     Example 22: The computer-readable storage media of example 21, wherein the computer-readable storage media further comprises computer-executable instructions that, when executed, cause the processor of the radar system to provide the track associated with the NLOS object as another input to the autonomous or semi-autonomous driving system. 
     Example 23: A computer-readable storage media comprising computer-executable instructions that, when executed, cause a processor of a radar system to perform as configured inf any of examples 1 through 14. 
     Example 24: A method comprising: identifying, using EM energy reflected by one or more objects and received by a receiver of a radar system, a detection of an object; determining, using map data, whether a direct-path reflection associated with the detection is located within a roadway corresponding to a travel path of a vehicle; responsive to a determination that the direct-path reflection associated with the detection is not located within the roadway, determining whether a multipath reflection associated with the detection is viable; responsive to a determination that the multipath reflection associated with the detection is viable, determining that the detection corresponds to a non-line-of-sight (NLOS) object; and providing an indication of the NLOS object as an input to an autonomous-driving or assisted-driving system. 
     CONCLUSION 
     While various embodiments of the disclosure are described in the foregoing description and shown in the drawings, it is to be understood that this disclosure is not limited thereto but may be variously embodied to practice within the scope of the following claims. From the foregoing description, it will be apparent that various changes may be made without departing from the scope of the disclosure as defined by the following claims.