Abstract
Autonomous vehicles have been envisioned for more than 100 years. One of the first suggestions was a front cover of Scientific America back in 1916. Today, it is possible to get cars that drive autonomously for extended distances. We are also starting to see micro-mobility solutions, such as the Nuro vehicles for pizza delivery. Building autonomous cars that can operate in urban environments with a diverse set of road-users is far from trivial. Early 2018 the Contextual Robotics Institute at UC San Diego launched an effort to build a full stack autonomous vehicle for micro-mobility. The motivations were diverse: i) development of a system for operation in an environment with many pedestrians, ii) design of a system that does not rely on dense maps (or HD-maps as they are sometimes named), iii) design strategies to build truly robust systems, and iv) a framework to educate next-generation engineers. In this paper, we present the research effort of design, prototyping, and evaluation of such a vehicle. From the evaluation, several research directions are explored to account for shortcomings. Lessons and issues for future work are additionally drawn from this work.
Highlights
Design of autonomous vehicles is not a new effort
Dickmanns developed an autonomous navigation stack based on computer vision techniques using the VaMoRs vehicle; early publications are from 1986 [1] and the project is summarized in [2]
Result: target speed v_target 1 v_target = v; 2 if auto_enabled 3 v_target = v_prev; 4 end 5 if v_target > speed_limit + 6 a = adecel; 7 else 8 a = aaccel; 9 end 10 v_target = v_target + a · dt contrast to our approach for speed keeping within the Forward state, the estimated acceleration and target speed generated during vehicle following and planned stops is dynamic by nature and requires an accurate formulation to stop within a specific distance for planned stops or to match the speed of the vehicle in front of the ego-vehicle
Summary
Design of autonomous vehicles is not a new effort. One of the earliest efforts was led by Dickmanns at the Universitaet der Bundeswehr in Munich. Extrinsic calibration was performed between each cameraLiDAR pair by manually identifying correspondences in the camera and LiDAR frames.
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