Automakers are rapidly taking their vehicles to the next level of connectivity and intelligence. And with breakthroughs such as C-V2X gaining momentum, auto manufacturers will need to source highly reliable and accurate position-location technology to ensure that vehicles safely and reliably interact with connected things such as other vehicles, road infrastructure, mobile devices in the hands of pedestrians and cyclists, and probably more. However, systems such as high-precision GNSS/INS system can cost tens of thousands of dollars, which prevents them from being adopted for broad commercial deployments.

In an effort to transform and accelerate the automobile industry and make roads safer for all, we recently developed Qualcomm Vision Enhanced Precise Positioning (VEPP) technology, which fuses multiple automotive sensors – Global Navigation Satellite Services (GNSS), a camera, an Inertial Measurement Unit (IMU), and wheel sensors — to offer more accurate global positioning of vehicles. Equally important for auto manufacturers is that VEPP is relatively affordable, using sensors already present in today’s vehicles.

VEPP accuracy and possible use cases

Our VEPP testing achieved lane-level accuracy — accurate to <1 meter. In comparison, GNSS-based solutions, possibly incorporating IMU and wheel sensors, can only achieve this type of accuracy in open-sky conditions. Furthermore, the GNSS-based solutions can be challenged in urban environments due to satellite signals encountering multi-path propagation, resulting in errors of several meters — quite critical in driving situations. VEPP, on the other hand, in such scenarios has demonstrated that a single camera — common in today’s new vehicles — is designed to play a critical role in achieving lane-level accuracy in GNSS challenged environments. VEPP does not have any dependency on the availability of any kind of map.

What’s the significance of lane-level accuracy? It enables a host of applications, critical to near-future automobile capabilities:

Lane-level navigation: in contrast to current navigation systems, lane-specific guidance can be provided. For example, VEPP is designed to help identify that the vehicle is on an exit-only lane, or a turn-only lane, and thereby guide the user to change lanes in order to stay on the road. Likewise, VEPP is designed to help determine the number of lanes that the vehicle needs to change in order to take an exit.
Lane-specific C-V2X warnings in urban areas: for instance, a broken-down car could alert approaching cars that it has stalled, or traffic signals could “tell” waiting cars time to green in either left-turn or drive through lanes.
Autonomous driving: requires vehicle location on a high definition (HD) map comprised of lanes and signs. Because of its accuracy, VEPP is designed to greatly simplify the processing step of associating lanes and sign information observed in the vehicle camera with corresponding landmarks in the HD map. This association step is critical for locating the vehicle to the sub decimeter accuracy, demanded by Level 3 and higher autonomy.
HD map crowdsourcing: as mentioned above, a critical component for autonomous driving is the availability of HD maps. In addition to generating these maps, there is the need for constantly updating maps due to changes in roadways. This is currently possible only through crowdsourcing of vehicle positions and road observations to the cloud for processing. VEPP’s accuracy offers simpler processing in addition to generating maps tethered to world coordinates.

To demonstrate VEPP, Qualcomm Technologies developed a board (see video above, 0:15-0:19) that consists of multiple components: a front-facing camera, an Inertial Measurement Unit (IMU), and a single frequency (L1) GNSS receiver¹connected to a vehicle Controller Area Network (CAN) bus. VEPP uses wheel sensor information from the CAN bus, specifically wheel speeds and steering angle. It also uses a Kalman filtering framework whose implementation is optimized on Qualcomm Snapdragon automotive processors.