6,224 research outputs found
A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
Vehicle to Vehicle (V2V) communication has a great potential to improve
reaction accuracy of different driver assistance systems in critical driving
situations. Cooperative Adaptive Cruise Control (CACC), which is an automated
application, provides drivers with extra benefits such as traffic throughput
maximization and collision avoidance. CACC systems must be designed in a way
that are sufficiently robust against all special maneuvers such as cutting-into
the CACC platoons by interfering vehicles or hard braking by leading cars. To
address this problem, a Neural- Network (NN)-based cut-in detection and
trajectory prediction scheme is proposed in the first part of this paper. Next,
a probabilistic framework is developed in which the cut-in probability is
calculated based on the output of the mentioned cut-in prediction block.
Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed
which incorporates this cut-in probability to enhance its reaction against the
detected dangerous cut-in maneuver. The overall system is implemented and its
performance is evaluated using realistic driving scenarios from Safety Pilot
Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I
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A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles
Sustainability, transport and design: reviewing the prospects for safely encouraging eco-driving
Private vehicle use contributes a disproportionately large amount to the degradation of the environment we inhabit. Technological advancement is of course critical to the mitigation of climate change, however alone it will not suffice; we must also see behavioural change. This paper will argue for the application of Ergonomics to the design of private vehicles, particularly low-carbon vehicles (e.g. hybrid and electric), to encourage this behavioural change. A brief review of literature is offered concerning the effect of the design of a technological object on behaviour, the inter-related nature of goals and feedback in guiding performance, the effect on fuel economy of different driving styles, and the various challenges brought by hybrid and electric vehicles, including range anxiety, workload and distraction, complexity, and novelty. This is followed by a discussion on the potential applicability of a particular design framework, namely Ecological Interface Design, to the design of in-vehicle interfaces that encourage energy-conserving driving behaviours whilst minimising distraction and workload, thus ensuring safety
Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control
Advanced Driver Assistance Systems (ADAS) are increasingly important in
improving driving safety and comfort, with Adaptive Cruise Control (ACC) being
one of the most widely used. However, pre-defined ACC settings may not always
align with driver's preferences and habits, leading to discomfort and potential
safety issues. Personalized ACC (P-ACC) has been proposed to address this
problem, but most existing research uses historical driving data to imitate
behaviors that conform to driver preferences, neglecting real-time driver
feedback. To bridge this gap, we propose a cloud-vehicle collaborative P-ACC
framework that incorporates driver feedback adaptation in real time. The
framework is divided into offline and online parts. The offline component
records the driver's naturalistic car-following trajectory and uses inverse
reinforcement learning (IRL) to train the model on the cloud. In the online
component, driver feedback is used to update the driving gap preference in real
time. The model is then retrained on the cloud with driver's takeover
trajectories, achieving incremental learning to better match driver's
preference. Human-in-the-loop (HuiL) simulation experiments demonstrate that
our proposed method significantly reduces driver intervention in automatic
control systems by up to 62.8%. By incorporating real-time driver feedback, our
approach enhances the comfort and safety of P-ACC, providing a personalized and
adaptable driving experience
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