To ensure safe cooperative driving in mixed traffic with both manned and unmanned vehicles, it is crucial to understand and model the driving styles of human drivers. This paper explores how to develop accurate recognition of driving style and use that for the prediction of vehicle motion, which enables better performance in cooperative driving. A simulation testbed that consists of a driving simulator and a copilot is first introduced for the purpose of data collection and testing. A Long Short-Term Memory (LSTM)-based network that models human driving styles and predicts driving acceleration is developed. Standalone tests are conducted to examine the model performance in the simulation testbed. Finally, the model is evaluated in a series of merging experiments that involves 5 vehicles.

Dexterous telemanipulation is crucial in advancing human-robot systems, especially in tasks requiring precise and safe manipulation. However, it faces significant challenges due to the physical differences between human and robotic hands, the dynamic interaction with objects, and the indirect control and perception of the remote environment. Current approaches predominantly focus on mapping the human hand onto robotic counterparts to replicate motions, which exhibits a critical oversight: it often neglects the physical interaction with objects and relegates the interaction burden to the human to adapt and make laborious adjustments in response to the indirect and counter-intuitive observation of the remote environment. This work develops an End-Effects-Oriented Learning-based Dexterous Telemanipulation (EFOLD) framework to address telemanipulation tasks. EFOLD models telemanipulation as a Markov Game, introducing multiple end-effect features to interpret the human operator’s commands during interaction with objects. These features are used by a Deep Reinforcement Learning policy to control the robot and reproduce such end effects. EFOLD was evaluated with real human subjects and two end-effect extraction methods for controlling a virtual Shadow Robot Hand in telemanipulation tasks. EFOLD achieved real-time control capability with low command following latency (delay<0.11s) and highly accurate tracking (MSE<0.084 rad).

Autonomous vehicles are gradually entering the transportation system. The traffic will become more heterogeneous since both autonomous and human-driven vehicles will share the roads. Cooperative driving, by promoting synchronized actions and shared situational awareness among vehicles, can significantly enhance driving safety. On the other hand, understanding human drivers is a pivotal step for cooperative driving in such mixed traffic environments, which facilitates effective interaction between human drivers and their vehicles. This paper presents a testbed that can be used to conduct research in intelligent and cooperative driving. The testbed consists of driving simulators, custom-designed copilots with an Artificial Intelligence engine, an optimization server, and a cloud database. The copilot is capable of sensing and understanding the human driver, the vehicle and the traffic. It can assist the driver by providing timely alerts on potential risks. Most importantly, it can communicate with other nearby vehicles for cooperative driving. Two case studies are presented to validate and evaluate the testbed. The first case study demonstrates the performance of the copilot in human distraction detection and driving assistance. The second case study focuses on cooperative driving between one human-driven vehicle and two connected autonomous vehicles in a lane-changing scenario. We expect this research testbed to be used in various research projects that involve human-driven vehicles and connected autonomous vehicles.

To achieve safe cooperative driving in mixed traffic of manned and unmanned vehicles, it is necessary to understand and model human drivers' driving behaviors. This paper proposed a Hidden Markov Model (HMM)-based method to analyze human driver's control and vehicle's dynamics; and then recognize the human driver's action, such as accelerating, braking, and changing lanes. With the knowledge of the human driver's actions, a probability model is used to predict the human-driven vehicle's acceleration. Such information on the driver behavior and the vehicle behavior can be used to achieve safer cooperative driving, which is realized using vehicle-to-vehicle (V2V) communication and model predictive control (MPC). The proposed method was tested and evaluated in our custom-built cooperative driving testbed. Experimental results show that the above driver action model is effective and accurate. A preliminary case study on a lane merging scenario is provided to further validate its effectiveness and capability.

Modeling a human-driven vehicle is a difficult subject since human drivers have a variety of stochastic behavioral components that influence their driving styles. We develop a cooperative driving framework to incorporate dif-ferent human behavior aspects, including the attentiveness of a driver and the tendency of the driver following advising commands. To demonstrate the framework, we consider the merging coordination between a human-driven vehicle and an autonomous vehicle (AV) in a connected environment. We propose a stochastic model predictive controller (sMPC) to address the stochasticity in human driving behavior and design coordinated merging actions to optimize the AV input and influence human driving behavior through advising commands. Simulation and human-in-the-loop (HITL) experimental results show that our formulation is capable of accommodating a distracted driver and optimizing AV inputs based on human driving behavior recognition.

This paper presents a cooperative driving testbed based on vehicle-to-vehicle (V2V) communication, which can be used for research in intelligent transportation systems, such as collision avoidance in mixed traffic of both human-driven vehicles and autonomous vehicles. To achieve the goal, an intelligent copilot is developed. The copilot can share the data regarding vehicle status, intention, etc, with other nearby vehicles through V2V communication. Several case studies are conducted to validate the proposed testbed and evaluate the performances of cooperative driving. When dangerous situations occur, the copilot solves the collision avoidance problem using Mixed Integer Programming (MIP), which either provides control commands to the autonomous vehicle, or advises the human driver to take action. Experimental results show that the safety and stability of the involved vehicles have been significantly enhanced. This cooperative driving testbed can be used by researchers to develop and test cooperative driving algorithms before they are deployed in real vehicles.

Bayesian estimators are commonly used to estimate the state of a system over time, whether the Kalman Filter (or its derivatives), a particle filter, a sliding window approach or batch-based approach. A common assumption with all these estimators is that the parameters of the dynamics and measurement noise sources are known a-priori. To overcome this weakness, previous approaches have simultaneously estimated the system state and the covariance of the inputs. All of these approaches, however, have made the assumption that the input noise is still uncorrelated over time. In this paper, we remove this assumption by inferring both system parameters (such as the parameters for a first-order Gauss-Markov process (FOGM)) and process noise covariance simultaneously. This estimation is performed using variational inference in conjunction with a Rauch-Tung-Striebel (RTS) smoother. We demonstrate significantly improved performance over a similar RTS-based approach that estimates Q but has the time constant of the FOGM added to the system state.

Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex setting. Our analysis show that the initial KL-divergence between the Markov Chain and the target posterior distribution is exponentially decreasing while the error contributions to the overall KL-divergence from the additive noise is decreasing in polynomial time. We further show that the polynomial-term experiences speed-up with number of agents and provide sufficient conditions on the time-varying step-sizes to guarantee convergence to the desired distribution. The performance of the proposed algorithm is evaluated on a wide variety of machine learning tasks. The empirical results show that the performance of individual agents with locally available data is on par with the centralized setting with considerable improvement in the convergence rate.

With a goal to improve transportation safety, this paper proposes a collaborative driving framework based on assessments of both internal and external risks involved in vehicle driving. The internal risk analysis includes driver drowsiness detection and driver intention recognition that helps to understand the human driver's behavior. Steering wheel data and facial expression are used to detect the driver's drowsiness. Hidden Markov models are adapted to recognize the driver's intention using the vehicle's lane position, control, and state data. For the external risk analysis, a co-pilot utilizes a collision avoidance system to estimate the collision probability between the ego vehicle and other nearby vehicles. Based on the risk analyses, we design a novel collaborative driving scheme by fusing the control inputs from the human driver and the co-pilot to obtain the final control input for the ego vehicle under different circumstances. The proposed collaborative driving framework is validated in an assisted-driving testbed, which enables both autonomous and manual driving capabilities.

The RANdom SAmple Consensus (RANSAC) algorithm, as a robust parameter estimator, has been widely used to remove gross errors. However, there is less work on analyzing the uncertainty produced by the RANSAC. This paper fills this gap by presenting an uncertainty estimation algorithm for the RANSAC. Based on a thorough analysis on the uncertainty of the model parameters generated during the random hypothesis sampling process of the RANSAC, we derive the probability that each hypothesis is selected as the best hypothesis by the RANSAC. Using the probability of the best hypothesis, we characterize the error expectation and error covariance of the model parameter estimates and compute the probability of each data point being an inlier. Three models including line fitting, homography, and essential matrix are used to evaluate the performance of the uncertainty estimation algorithm. Results demonstrate that the uncertainty produced by the RANSAC is characterized successfully by the proposed algorithm.

We consider a group of agents collaboratively transporting a flexible payload. The contact forces between the agents and the payload are modeled as gradients of nonlinear potentials that describe the deformations of the payload. The load-transport problem is then treated in a similar fashion to the formation-control problem. Decentralized control laws are developed such that without explicit communication, the agents and the payload converge to the same constant velocity; meanwhile, the contact forces are regulated. Experimental results illustrate the effectiveness of our designs.