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7 Reasons Every Autonomous Vehicle Needs an Accurate Inertial Measurement Unit

The IMU’s independent property serves it well for safety and sensor fusion applications, but that is just one of the ways it lends advantages to self-driving solutions.

An inertial measurement unit (IMU) is a device that directly measures the three linear acceleration components and the three rotational rate components (6-DOF) of a vehicle. An IMU is unique among the sensors typically found in an autonomous vehicle because an IMU requires no connection or knowledge of the external world.

A self-driving car requires many different technologies: LiDAR to create a precise 3-D image of the local surroundings; RADAR for ranging targets using a different part of the EM spectrum; cameras to read signs and detect color; high-definition maps for localization, and more. Each of these technologies relies on the external environment in order to send localization, perception, and control data to the software stack. The IMU has no such reliance, and its unique “independent” nature makes it a core technology for both safety and sensor-fusion.

Figure 1: An accurate IMU can mitigate depth and ambiguity and other issues associated with existing technologies.

#1 Safety First
The system engineer needs to consider every scenario and always have a back-up plan. Failure Mode Effects Analysis (FMEA) formalizes this approach into design requirements for risk mitigation. FMEA will ask what happens if the LiDAR, RADAR, and cameras all fail at the same time? An IMU can dead-reckon for a short period of time, meaning it can determine full position and attitude independently for a short while. An IMU alone can slow the vehicle down in a controlled way and bring it to a stop—achieving the best practical outcome in a bad situation. While this may seem like a contrived requirement, it turns out to be a fundamental one to a mature safety approach.

#2 A Good Attitude
An accurate IMU can determine and track attitude precisely. We often think of a car’s position or location, but when driving the direction or heading is equally crucial. Dynamic control of the vehicle requires sensors with dynamic response, and an IMU does a nice job of tracking dynamic attitude changes accurately. Moreover, attitude is needed to control the vehicle and is often an input into other algorithms. While LiDAR and cameras can be useful in determining attitude, GPS is often pretty useless. Moreover, a stable independent attitude reference has value in calibration and alignment.

#3 Accurate Lane Keeping
When not distracted or drunk, humans are pretty good at driving. A typical driver can hold their position in a lane to less than 10cm. If an autonomous vehicle wanders in its lane, then it will appear to be a bad driver. During a turn, for instance, poor lane keeping could easily result in an accident. The IMU is a key dynamic sensor to steer the vehicle dynamically, and the IMU can maintain a better than 30cm accuracy level for short periods (up to 10 seconds) when other sensors go offline. The IMU is also used in algorithms that can cross compare multiple ways to determine position/location and then assign a certainty to the overall localization estimate. Without the IMU, it may be impossible to even know when the location error from a LIDAR solution has degraded.

Figure 2: During turns, an accurate IMU plays a key role in lane keeping.

#4 LiDAR is Still Expensive
Tesla is famous for its “No LiDAR required” approach to autopilot technology. If you don’t have LiDAR, a good IMU is even more critical because camera-based localization of the vehicle will have more frequent periods of low accuracy, simply depending on what is in the camera scene or the external lighting conditions. Camera-based localization uses Software Implemented Fault Tolerance (SIFT) feature tracking in the captured images to compute attitude. If the camera is not stereo (often the case) inertial data from the IMU itself is also a core part of the math to compute the position and attitude in the first place.

#5 Compute is Not Free
The powerful combination of high-accuracy LIDAR and high-definition maps is at the core of the most advanced Level 4 self-driving approaches such as those being tested by Cruise and Waymo. In these systems LiDAR scans are matched in real-time to the HD map using convolutional signal processing techniques. Based on the match, the precise location of vehicle and attitude is estimated. This process is computationally expensive. While we all like to believe the cost of compute is vanishingly small, on a vehicle it simply is not that cheap. The more accurately the algorithm knows its initial position and attitude, the less computation is required to compute the best match. In addition, by using IMU data, the risk of the algorithm getting stuck in a local minimum of HD map data is reduced.

#6 GPS/INS: Making High-Accuracy GPS Work
In today’s production vehicles GPS systems use low-cost single-frequency receivers, rendering the GPS accuracy pretty useless for vehicle automation. However, low-cost multi-frequency, network-corrected GPS is on the way from a wide variety of silicon suppliers. On top of this upcoming silicon, network correction-based solutions such as Real Time Kinematic (RTK) and Precise Point Positioning (PPP) can provide GPS fixes to centimeter-level accuracy under ideal conditions. However, these solutions are very sensitive to bridges, trees, buildings, and other features of the environment. It is well established that the way to overcome this challenge and improve high-accuracy GPS reliability is to use high-accuracy IMU aiding at a low-level in the position solution. Such GPS/INS techniques include tightly coupled and ultra-tightly coupled GPS/INS, expected to be available in Q4 2018 for the automotive market.

#7 Cars Already Need an IMU
Turns out production automobiles already have anywhere from 1/3 of an IMU to a full IMU on board. Vehicle stability systems rely heavily on a Z-axis gyro and lateral X-Y accelerometers. Roll over detection relies on a gyro mounted with its sensitive axis in the direction of travel. These sensors have been part of the vehicles’ safety systems for over a decade now. The only problem is that the sensor accuracy is typically too low to be of use for the prior six use cases. So why not upgrade the vehicle to a high-accuracy IMU and let it drive independently? The main barrier is cost.

ACEINNA, along with other companies in the industry, is working hard to remove cost barriers in the way of high-accuracy IMUs and the benefits they hold for autonomous vehicles.

Figure 3: ACEINNA’s IMUs are pushing the boundary of price-performance.


Mike Horton is the CTO of ACEINNA, which provides MEMS-based sensing solutions. Horton is responsible for corporate technology strategy and inertial-navigation related technology development. Prior to ACEINNA, he founded Crossbow Technology, a leader in MEMS-based inertial navigation systems and wireless sensor networks, with his advisor the late Dr. Richard Newton, while at UC Berkeley. Crossbow Technology grew to $23M in revenue prior to being sold in two transactions (Moog, Inc and MEMSIC) totaling $50M. In addition to his role at ACEINNA, Horton is active as an angel investor with two Silicon Valley based angel groups—Band of Angels and Sand Hill Angels. He also actively mentors young entrepreneurs in the UC Berkeley research community. He holds more than 15 patents and holds a BSEE and MSEE from UC Berkeley.

 

 

 

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