Zobrazeno 1 - 10
of 1 400
pro vyhledávání: '"Gerdes J"'
Automated drifting presents a challenge problem for vehicle control, requiring models and control algorithms that can precisely handle nonlinear, coupled tire forces at the friction limits. We present a neural network architecture for predicting fron
Externí odkaz:
http://arxiv.org/abs/2407.13760
Automated vehicles need to estimate tire-road friction information, as it plays a key role in safe trajectory planning and vehicle dynamics control. Notably, friction is not solely dependent on road surface conditions, but also varies significantly d
Externí odkaz:
http://arxiv.org/abs/2407.12989
Autor:
Weiss, Elliot, Gerdes, J. Christian
Rendering accurate multisensory feedback is critical to ensure natural user behavior in driving simulators. In this work, we present a virtual reality (VR)-based Vehicle-in-the-Loop (ViL) simulator that provides visual, vestibular, and haptic feedbac
Externí odkaz:
http://arxiv.org/abs/2203.03043
We present Contingency Model Predictive Control (CMPC), a motion planning and control framework that optimizes performance objectives while simultaneously maintaining a contingency plan -- an alternate trajectory that avoids a potential hazard. By pr
Externí odkaz:
http://arxiv.org/abs/2102.12045
Autor:
Leung, Karen, Schmerling, Edward, Zhang, Mengxuan, Chen, Mo, Talbot, John, Gerdes, J. Christian, Pavone, Marco
Publikováno v:
International Journal of Robotics Research, vol. 39, no. 10-11, pp. 1326--1345, 2020
Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is ensuring safety on the road -- a key challenge in doing so is accounting
Externí odkaz:
http://arxiv.org/abs/2012.03390
Publikováno v:
IEEE American Control Conference (ACC) (2019) 717-722
We present Contingency Model Predictive Control (CMPC), a novel and implementable control framework which tracks a desired path while simultaneously maintaining a contingency plan -- an alternate trajectory to avert an identified potential emergency.
Externí odkaz:
http://arxiv.org/abs/1903.08818
Publikováno v:
2015 American Control Conference
Iterative learning control has been successfully used for several decades to improve the performance of control systems that perform a single repeated task. Using information from prior control executions, learning controllers gradually determine ope
Externí odkaz:
http://arxiv.org/abs/1902.00611
Publikováno v:
Journal of Dynamic Systems, Measurement, and Control. SEPTEMBER 2016, Vol. 138
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 prob
Externí odkaz:
http://arxiv.org/abs/1902.00606
As autonomous vehicles (AVs) inch closer to reality, a central requirement for acceptance will be earning the trust of humans in everyday driving situations. In particular, the interaction between AVs and pedestrians is of high importance, as every h
Externí odkaz:
http://arxiv.org/abs/1902.00597
Autor:
Leung, Karen, Schmerling, Edward, Chen, Mo, Talbot, John, Gerdes, J. Christian, Pavone, Marco
Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is ensuring safety on the road --- a key challenge in doing so is accountin
Externí odkaz:
http://arxiv.org/abs/1812.11315