Robust Accelerating Control for Consistent Node Dynamics in a Platoon of CAVs

Driving as a platoon has potential to significantly benefit traffic capacity and safety. To generate more identical dynamics of nodes for a platoon of automated connected vehicles (CAVs), this chapter presents a robust acceleration controller using a multiple model control structure. The large uncertainties of node dynamics are divided into small ones using multiple uncertain models, and accordingly multiple robust controllers are designed. According to the errors between current node and multiple models, a scheduling logic is proposed, which automatically selects the most appropriate candidate controller into loop. Even under relatively large plant uncertainties, this method can offer consistent and approximately linear dynamics, which simplifies the synthesis of upper level platoon controller. This method is validated by comparative simulations with a sliding model controller and a fixed H∞ controller

Analysis on Causes and Countermeasures of Bullwhip Effect

Bullwhip effect is an inevitable phenomenon in supply chain management, because of its objective existence. This phenomenon is very common and harmful to make the operating costs of enterprises double and become one of the main concerns of many enterprises. In this paper, the causes of the bullwhip effect are explored through the methods of literature research and investigated consultation to weaken the bullwhip effect. This paper analyzes the key countermeasures with Wal-Mart successful logistics management case. And according to the reason of bullwhip effect, a mathematical programming model of maximizing the efficiency of supply chain is established, which provides a way to solve the negative effect of bullwhip effect and has certain reference value

Driver-automation indirect shared control of highly automated vehicles with intention-aware authority transition

Shared control is an important approach to avoid the driver-out-of-the-loop problems brought by imperfect autonomous driving. Steer-by-wire technology allows the mechanical decoupling between the steering wheel and the road wheels. On steer-by-wire vehicles, the automation can join the control loop by correcting the driver steering input, which forms a new paradigm of shared control. The new framework, under which the driver indirectly controls the vehicle through the automation’s input transformation, is called indirect shared control. This paper presents an indirect shared control system, which realizes the dynamic control authority allocation with respect to the driver’s authority intention. The simulation results demonstrate the effectiveness and benefits of the proposed control authority adaptation method

Feasible Policy Iteration

Safe reinforcement learning (RL) aims to solve an optimal control problem under safety constraints. Existing $\textit{direct}$ safe RL methods use the original constraint throughout the learning process. They either lack theoretical guarantees of the policy during iteration or suffer from infeasibility problems. To address this issue, we propose an $\textit{indirect}$ safe RL method called feasible policy iteration (FPI) that iteratively uses the feasible region of the last policy to constrain the current policy. The feasible region is represented by a feasibility function called constraint decay function (CDF). The core of FPI is a region-wise policy update rule called feasible policy improvement, which maximizes the return under the constraint of the CDF inside the feasible region and minimizes the CDF outside the feasible region. This update rule is always feasible and ensures that the feasible region monotonically expands and the state-value function monotonically increases inside the feasible region. Using the feasible Bellman equation, we prove that FPI converges to the maximum feasible region and the optimal state-value function. Experiments on classic control tasks and Safety Gym show that our algorithms achieve lower constraint violations and comparable or higher performance than the baselines

Understanding Driver Response Patterns to Mental Workload Increase in Typical Driving Scenarios

As vehicles become more complex and traffic increases, the associated mental workload of driving should increase, potentially compromising driving safety. As mental workload increases (as measured by the detection response time task), does how people drive (as assessed by driving performance and eye fixations) change? How does driving experience impact on such response patterns? To address those questions, data were collected in a motion-based driving simulator. Two driving scenarios were examined, a stop-controlled intersection (high workload — 16 participants, 320 trials) and speed-limited highway (low workload — 11 participants, 264 trials). In each scenario, in half of the trials, the participants were required to complete or not to complete a distracting secondary task. Hierarchical cluster analysis was used to identify driver response patterns. For highway driving, they are: (1) increased eye fixation variability and unchanged driving performance, and (2) unchanged fixation variability and increased mean speed. For intersection driving, they are: (1) increased and (2) decreased fixation variability both with decreased speed (mean and variance), and (3) increased fixation variability with increased speed. Eye fixation variability was more strongly associated with increased mental workload than other driving performance statistics. Furthermore, in contrast to prior research, changes in driving performance and eye fixations were not necessarily correlated with each other as mental workload increased. Novice drivers exhibit higher gaze variability, and they are more prone to maintain vehicle control than experienced drivers

Stability and scalability of homogeneous vehicular platoon: study on the influence of information flow topologies

In addition to decentralized controllers, the information flow among vehicles can significantly affect the dynamics of a platoon. This paper studies the influence of information flow topology on the internal stability and scalability of homogeneous vehicular platoons moving in a rigid formation. A linearized vehicle longitudinal dynamic model is derived using the exact feedback linearization technique, which accommodates the inertial delay of powertrain dynamics. Directed graphs are adopted to describe different types of allowable information flow interconnecting vehicles, including both radar-based sensors and vehicle-to-vehicle (V2V) communications. Under linear feedback controllers, a unified internal stability theorem is proved by using the algebraic graph theory and Routh-Hurwitz stability criterion. The theorem explicitly establishes the stabilizing thresholds of linear controller gains for platoons, under a large class of different information flow topologies. Using matrix eigenvalue analysis, the scalability is investigated for platoons under two typical information flow topologies, i.e., 1) the stability margin of platoon decays to zero as 0(1/N2) for bidirectional topology; and 2) the stability margin is always bounded and independent of the platoon size for bidirectional-leader topology. Numerical simulations are used to illustrate the results