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Found 19 papers, 7 papers with code
So you think you can track?
This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4. 2 mile stretch of 8-10 lane interstate highway near Nashville, TN.
Car-Following Models: A Multidisciplinary Review
Car-following (CF) algorithms are crucial components of traffic simulations and have been integrated into many production vehicles equipped with Advanced Driving Assistance Systems (ADAS).
Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset
Decentralized multiagent planning has been an important field of research in robotics.
A Hierarchical MPC Approach to Car-Following via Linearly Constrained Quadratic Programming
Single-lane car-following is a fundamental task in autonomous driving.
A rigorous multi-population multi-lane hybrid traffic model and its mean-field limit for dissipation of waves via autonomous vehicles
The choice of these parameters in the lane-change mechanism is critical to modeling traffic accurately, because different parameter values can lead to drastically different traffic behaviors.
Multi-Adversarial Safety Analysis for Autonomous Vehicles
This work in progress considers reachability-based safety analysis in the domain of autonomous driving in multi-agent systems.
Composing MPC with LQR and Neural Network for Amortized Efficiency and Stable Control
The proposed scheme does not require pre-computation and can improve the amortized running time of the composed MPC with a well-trained neural network.
Limitations and Improvements of the Intelligent Driver Model (IDM)
First it is shown that, for a specific class of initial data, the vehicles' velocities become negative or even diverge to $-\infty$ in finite time, both undesirable properties for a car-following model.
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms
We benchmark commonly used multi-agent deep reinforcement learning (MARL) algorithms on a variety of cooperative multi-agent games.
A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction
We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
We call our technique Protagonist Antagonist Induced Regret Environment Design (PAIRED).
Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL
We apply multi-agent reinforcement algorithms to this problem and demonstrate that significant improvements in bottleneck throughput, from 20\% at a 5\% penetration rate to 33\% at a 40\% penetration rate, can be achieved.
Robust Reinforcement Learning using Adversarial Populations
Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are perturbed.
Out-of-Distribution Generalization
reinforcement-learning
+1
Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehicles
We then directly transfer this policy without any tuning to the University of Delaware Scaled Smart City (UDSSC), a 1:25 scale testbed for connected and automated vehicles.
Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games
We study a general adversarial online learning problem, in which we are given a decision set X' in a reflexive Banach space X and a sequence of reward vectors in the dual space of X.
Adaptive Averaging in Accelerated Descent Dynamics
This dynamics can be described naturally as a coupling of a dual variable accumulating gradients at a given rate $\eta(t)$, and a primal variable obtained as the weighted average of the mirrored dual trajectory, with weights $w(t)$.
Minimizing Regret on Reflexive Banach Spaces and Learning Nash Equilibria in Continuous Zero-Sum Games
Under the assumption of uniformly continuous rewards, we obtain explicit anytime regret bounds in a setting where the decision set is the set of probability distributions on a compact metric space $S$ whose Radon-Nikodym derivatives are elements of $L^p(S)$ for some $p > 1$.
Accelerated Mirror Descent in Continuous and Discrete Time
We study accelerated mirror descent dynamics in continuous and discrete time.
