Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions.Comment: Accepted to 2019 IEEE Intelligent Transportation Systems Conferenc

Effects of Transport Delays of Manual Control System Performance

Throughput or transport delays in manual control systems can cause degraded performance and lead to potentially unstable operation. With the expanding use of digital processors, throughput delays can occur in manual control systems in a variety of ways such as in digital flight control systems in real aircraft, and in equation of motion computers and computer generated images in simulators. Research has shown the degrading effect of throughput delays on subjective opinion and system performance and dynamic response. A generic manual control system model is used to provide a relatively simple analysis of and explanation for the effects of various types of delays. The consequence of throughput delays of some simple system architectures is also discussed

Influence of the number of predecessors in interaction within acceleration-based flow models

In this paper, the stability of the uniform solutions is analysed for microscopic flow models in interaction with $K\ge1$ predecessors. We calculate general conditions for the linear stability on the ring geometry and explore the results with particular pedestrian and car-following models based on relaxation processes. The uniform solutions are stable if the relaxation times are sufficiently small. The analysis is focused on the relevance of the number of predecessors in the dynamics. Unexpected non-monotonic relations between $K$ and the stability are presented.Comment: 18 pages, 14 figure

Feedforward control of an active seat for dynamic driving simulators

The Active Seat system is used to overcome the lacking of low-frequency sustained accelerations on dynamic driving simulators. The system provides artificial pressure cues to trick the driver's sensory system to feel an increased acceleration. Different ways are explored: the reproduction of the pressure stimuli of a real vehicle or the creation of an haptic feedback of the acceleration vector. The control schemes are developed taking into account the pressure induced by the platform movement

Aerospace medicine and biology. A continuing bibliography with indexes, supplement 195

This bibliography lists 148 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1979