Emerging wireless and cellular 5G technologies are bringing a new level of connectivity to cars and enabling many applications to support safety, efficiency, and Internet access. Connectivity is a natural complement to other kinds of automotive sensors that are also being integrated in vehicles. Communication allows vehicles to exchange what they see and allows vehicles to expand their sensing range thus making better automated decisions. While state-of-the-art radar and communication technology is quickly making its way to the validation and production test floors, engineers and scientists are actively working on innovative ideas to better understand how automated cars will better interact with non-automated objects on the road, drive synergy between vehicular radar and communications, and design wide bandwidth radars capable of resolving very short distances. To bring these technologies to market, the automotive industry needs to leverage a software-centric platform-based approach that helps accelerate the design, characterization, and test phases.
Trends in Connected Car Technologies
Wireless communication is bringing a new level of connectivity to cars. With wireless, cars may communicate with each other directly in vehicle-to-vehicle (V2V) mode, or through the infrastructure in vehicle-to-infrastructure (V2I) mode. There are many applications of connectivity to support safety, transportation efficiency, and of course Internet access. Connectivity is a natural complement to other kinds of automotive sensors that are being integrated in vehicles.
Vehicular connectivity has been investigated for at least twenty years. The defacto approach is dedicated short range communication (DSRC). This technology is envisioned primarily as a means for exchanging basic safety messages, though it also has some applications in traffic management. DSRC supports both V2V and V2I. After nearly 20 years of development, DSRC is now available in some US cars, though wide use will probably not been seen without a government mandate.
Automated vehicles come in many flavors, depending on the level of automation. At one extreme is no automation, where the driver is in full control. At the other extreme, the vehicle is in complete control and there are no controls for any human assistance. At levels in between, certain driving functions are automated but the human may still be in charge. For example, in a lower level of automation, the driver may be warned about a potential forward collision. In a higher level of automation, the car may automatically apply the brakes and may also take evasive action to avoid the collision. While fully automated driving is often called “autonomous driving,” it is unlikely that full automation can happen simultaneously with full autonomy, which implies no communication. The reason is that fully automated high-speed driving is difficult without communication of high resolution map data.
Connectivity is a critical component of vehicle automation because it enhances the sensing range of vehicles. Sensors being deployed for automation including automotive radar, visual cameras, and LIDAR. Radar is used for automatic cruise control, forward collision warning, lane change assistance, parking, and pre-crash applications. Visual cameras are used for safe backups, monitoring blind spots, nap prevention, and lane keeping. LIDAR provides high resolution 3D map information that can be used for autonomous navigation, as well as pedestrian and bicycle detection. All three technologies are important for fully automated vehicles. For example, Tesla uses visual cameras for automated highway driving while Google cars make heavy use of LIDAR and 3D map data for accurate driving and navigation, and several radars to aid in the detection of other vehicles. The range of each technology depends on its configuration and the deployment scenario. For example, in rural areas, radar may achieve 200m, LIDAR 35 m, and visual cameras 30m, but in urban areas, the range of all these technologies diminishes to a few meters due to obstruction by other vehicles. Essentially, these external sensors are limited by what they can see. Communication allows vehicles to expand their sensing range by leveraging what can be seen by other vehicles in the front, back, or sides.
Mixed-use environments where vehicles have different levels of automation and communication remains a challenge. One approach is to deploy sensing at the base station, for example radar, visual cameras, or LIDAR. Then the information derived from sensors can be broadcast to connected vehicles, giving them situational awareness about non-connected vehicles, and non-vehicular users of roadways. The infrastructure based approach has the advantage that it works well even if most other vehicles do not have communication capability. Infrastructure will also make higher levels of automation more effective, for example to coordinate interactions of vehicles through intersections without the need for traffic lights. This infrastructure-based sensing will likely be built around 5G cellular communications, since they aim to provide much higher data rates.
At present there is tremendous interest in the automotive use case for 5G. Applications include vehicular automation, transportation planning and operations, and of course infotainment. 5G will support 10x lower latencies and 10x higher bandwidths than 4G solutions making it especially suitable for automotive applications. In particular, millimeter wave 5G is especially attractive because of very high data rates, which can be used for the exchange of raw sensor data. Millimeter wave for automotive applications is one of several ongoing research topics at The University of Texas at Austin. Other topics include the co-design of communication and radar, the use of low frequency communication signals as a low-cost means of automotive radar, and the use of sensing-based infrastructure to aid millimeter wave communication.
Platform based approach accelerates innovation and shortens time to market
Typical engineering projects go through phases of design, characterization, and test. These phases are typically disjoint, with different tools and techniques used in each. To drive development efficiency in each phase and rapid transition to the next, NI provides a platform-based approach, which reduces barriers between each phase with a unifying environment based on common, well-integrated hardware and software components. At the heart of NI’s platform is LabVIEW software. This platform based approach accelerates productivity and reduces time spent in each phase.