Showing posts from June, 2019

Event Detection Considerations for Autonomous Vehicles (OEDR -- part 2)

Object and Event Detection and Recognition (OEDR) also involves making predictions about what might happen next. Is that pedestrian waiting for a bus? or about to walk out into the crosswalk right in front of my car? Did you think of all of these aspects? The infamous Pittsburgh Left. The first vehicle at a red light will (sometimes) turn left when the light turns to green. Some factors to consider when deciding what events and behaviors your system needs to predict include: Determining expected behaviors of other objects, which might involve a probability distribution and is likely to be based on object classification. Normal or reasonably expected movements by objects in the environment. Unexpected, incorrect, or exceptional movement of other vehicles, obstacles, people, or other objects in the environment. Failure to move by other objects which are reasonably expected to move. Operator interactions prior to, during, and post autonomy engagement including: supervising

Object Detection Considerations for Autonomous Vehicles (OEDR -- part 1)

Object and Event Detection and Recognition (OEDR) involves having an autonomous vehicle detect and classify various types of objects so that it can plan a response. Detection is only the first step; you need to also be able to classify the obstacle to predict what might happen next. Pedestrians tend to walk into the roadway. Bushes, not so much. Did you think of all of these aspects? Q: Why did the Mr. Rogers-saurus cross the street? A: Trick question; he doesn't actually move because he is part of the Pittsburgh Dinosaur Parade. Some factors to consider when deciding what objects your system needs to detect and recognize include: Ability to detect and identify (e.g. classify) all relevant objects in the environment. Processing and thresholding of sensor data to avoid both false positives (e.g., bouncing drink can, steel bridge joint, steel road construction cover plate, roadside sign, dust cloud, falling leaves) and false negatives (e.g., highly publicized partially auto

Operational Design Domain (ODD) for Autonomous Systems

The Operational Design Domain (ODD) is the set of environmental conditions that an autonomous system is designed to work in. Typically an ODD is thought of as some sort of geo-fencing plus a obvious weather conditions (rain, snow, sun). But, it's a lot more than that. Did you think of all of these? Canton Avenue, the unofficial steepest street in the world, is less than 4 miles from downtown Pittsburgh. Note cobblestone on the top half and the sidewalk stairs.  Cars slide (sometimes backwards) down the street in winter. Geo-fencing is more complicated than drawing a circle around a city center. [Wikipedia] Characterizing the system operational environment should include at least the following: Operational terrain, and associated location-dependent characteristics (e.g., slope, camber, curvature, banking, coefficient of friction, road roughness, air density) including immediate vehicle surroundings and projected vehicle path. It is important to note that dramatic chan