5,672 research outputs found
Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments
Traffic waves are phenomena that emerge when the vehicular density exceeds a
critical threshold. Considering the presence of increasingly automated vehicles
in the traffic stream, a number of research activities have focused on the
influence of automated vehicles on the bulk traffic flow. In the present
article, we demonstrate experimentally that intelligent control of an
autonomous vehicle is able to dampen stop-and-go waves that can arise even in
the absence of geometric or lane changing triggers. Precisely, our experiments
on a circular track with more than 20 vehicles show that traffic waves emerge
consistently, and that they can be dampened by controlling the velocity of a
single vehicle in the flow. We compare metrics for velocity, braking events,
and fuel economy across experiments. These experimental findings suggest a
paradigm shift in traffic management: flow control will be possible via a few
mobile actuators (less than 5%) long before a majority of vehicles have
autonomous capabilities
Observability analysis and optimal sensor placement in stereo radar odometry
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Localization is the key perceptual process closing the loop of autonomous navigation, allowing self-driving vehicles to operate in a deliberate way. To ensure robust localization, autonomous vehicles have to implement redundant estimation processes, ideally independent in terms of the underlying physics behind sensing principles. This paper presents a stereo radar odometry system, which can be used as such a redundant system, complementary to other odometry estimation processes, providing robustness for long-term operability. The presented work is novel with respect to previously published methods in that it contains: (i) a detailed formulation of the Doppler error and its associated uncertainty; (ii) an observability analysis that gives the minimal conditions to infer a 2D twist from radar readings; and (iii) a numerical analysis for optimal vehicle sensor placement. Experimental results are also detailed that validate the theoretical insights.Peer ReviewedPostprint (author's final draft
A Sequential Two-Step Algorithm for Fast Generation of Vehicle Racing Trajectories
The problem of maneuvering a vehicle through a race course in minimum time
requires computation of both longitudinal (brake and throttle) and lateral
(steering wheel) control inputs. Unfortunately, solving the resulting nonlinear
optimal control problem is typically computationally expensive and infeasible
for real-time trajectory planning. This paper presents an iterative algorithm
that divides the path generation task into two sequential subproblems that are
significantly easier to solve. Given an initial path through the race track,
the algorithm runs a forward-backward integration scheme to determine the
minimum-time longitudinal speed profile, subject to tire friction constraints.
With this fixed speed profile, the algorithm updates the vehicle's path by
solving a convex optimization problem that minimizes the resulting path
curvature while staying within track boundaries and obeying affine,
time-varying vehicle dynamics constraints. This two-step process is repeated
iteratively until the predicted lap time no longer improves. While providing no
guarantees of convergence or a globally optimal solution, the approach performs
very well when validated on the Thunderhill Raceway course in Willows, CA. The
predicted lap time converges after four to five iterations, with each iteration
over the full 4.5 km race course requiring only thirty seconds of computation
time on a laptop computer. The resulting trajectory is experimentally driven at
the race circuit with an autonomous Audi TTS test vehicle, and the resulting
lap time and racing line is comparable to both a nonlinear gradient descent
solution and a trajectory recorded from a professional racecar driver. The
experimental results indicate that the proposed method is a viable option for
online trajectory planning in the near future
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