ADVERTISEMENT FEATURE COVER STORY DRIVER ASSISTANCE TO
DRIVER REPLACEMENT The cognitive vehicle demands
high-integrity sensor data Words by Chris Jacobs, vice president, autonomous transportation and safety, Analog Devices
I
t’s the moonshot of our time. From sensors to artificial intelligence (AI), the electronics supply chain has formed a collaborative matrix dedicated to making autonomous vehicles safe. To that end, there is much to be done in hardware and software development to ensure drivers, passengers and pedestrians are protected. While machine learning and AI have a role to play, their effectiveness depends upon the quality of the incoming data. As such, no autonomous vehicle can be considered safe unless it is built upon a foundation of high- performance, high-integrity sensor signal chains, to consistently supply the most accurate data upon which to base life or death decisions. There are many obstacles on the road
to safe autonomous vehicles. However, the goal has been set and the imperative is clear: vehicle autonomy is coming and safety is paramount. Putting collaboration and new thinking first, automotive manufacturers are talking directly to silicon vendors; sensor makers are discussing sensor fusion with AI algorithm developers, and software developers are connecting with hardware providers to get the best out of both. Old relationships are changing and new ones are forming dynamically to optimise the combination of performance, functionality, reliability, cost and safety in the final design. These sensor technologies include cameras, light-detection-and-ranging (LIDAR), radio detection and ranging (RADAR), microelectromechanical systems (MEMS) and inertial measurement units (IMUs) ultrasound and GPS: all provide the critical inputs for AI systems that will drive the truly cognitive autonomous vehicle.
8 SEPTEMBER 2019 | ELECTRONICS
COGNITIVE VEHICLES FOUNDATIONAL TO PREDICTIVE SAFETY
Vehicle intelligence is often expressed as Levels of Autonomy. Levels 1 and 2 are largely warning systems, whereas at Level 3 and above, the vehicle can take action to avoid an accident. As the vehicle progresses to Level 5, the steering wheel is removed and the car operates fully autonomously. In these first few system generations, as vehicles start to take on Level 2 functionality, the sensor systems operate independently. These warning systems have a high false alarm rate and are often turned off. To reach full cognitive autonomy, the number of sensors increases significantly. Additionally, their performance and response times must quickly improve.
Various sensing modalities are used for perception and navigation vehicles for ADAS. They tend to work independently and simply provide warnings to drivers, so that they can adapt accordingly
ENSCO Aerospace Sciences and Engineering Division, advances in sensing modalities allow an automobile to not only recognise the current state of the environment, but also be aware of its history. This can be as simple as awareness of road conditions, such as the location of potholes, or as detailed as the types of accidents and how they occurred in a certain area over time. At the time of these cognitive concepts’ development, the level of sensing, processing, memory capacity and connectivity made them seem far- fetched; but much has changed. Now, this historical data can be accessed and factored into real-time data from the vehicle’s sensors, to provide increasingly accurate degrees of preventive action and incident avoidance.
For example, an IMU can detect a sudden bump or swerve indicating a pothole or an obstacle. In the past, there was nowhere to go with this information; now real-time connectivity allows this data to be sent to a central database and used to warn other vehicles of the hole or obstacle, and likewise with other sensor data. This data is continually gathered and compiled. It is then fed into ever- improving algorithms to perform predictive analysis, based on both the current and historical state of the vehicle and the environment in which it operates. Never tiring but always learning, adapting and improving, the cognitive vehicle will make increasingly optimal and safer decisions, to the point that they will in all likelihood surpass those of humans, in many situations.
With more sensors built into vehicles,
they can also better monitor and factor in current mechanical conditions, such as tyre pressure, change in weight (e.g. loaded versus unloaded, one passenger or six), as well as other wear and tear factors that might affect braking and handling. With more external sensing modalities, the vehicle can become more aware of its health and surroundings. Borrowing principles developed by Dr. Joseph Mitola, chief technologist,
A fully cognitive vehicle that is aware of both the present and historical state and nature of its surroundings, as well as its own state (position, rate of speed, trajectory, and mechanical condition) is necessary for safe autonomous vehicles
MULTI-FACETED DECISION MAKING AND ANALYSIS
Much progress has been made in advancing the state of the art of vehicle perception. The emphasis is on gathering the data from the various sensors and applying sensor fusion strategies to maximise their complementary strengths, supporting their respective weaknesses under various conditions. Still, even the best machine learning algorithms require ~300ms to make
/ ELECTRONICS
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