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Connected and automated vehicles (CAVs) can be beneficial for improving the operation of highway bottlenecks such as weaving sections. This paper proposes a bi-level control approach based on an upper-level deep reinforcement learning controller and a lower-level model predictive controller to coordinate the lane-changings of a mixed fleet of CAVs and human-driven vehicles (HVs) in weaving sections. The upper level represents a roadside controller that collects vehicular information from the entire weaving section and determines the control weights used in the lower-level controller. The lower level is implemented within each CAV, which takes the control weights from the upper-level controller and generates the acceleration and steering angle for individual CAVs based on the local situation. The lower-level controller further incorporates an HV trajectory predictor, which is capable of handling the dynamic topology of vehicles in weaving scenarios with intensive mandatory lane changes. The case study inspired by a real weaving section in Basel, Switzerland, shows that our method consistently outperforms state-of-the-art benchmarks., Comment: 12 pages, 8 figures

High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in mixed-traffic high-speed cruising scenarios by proposing an integrated framework that synthesizes three fundamental modules, i.e., behavioral decision-making, path-planning, and motion-control modules. Considering that the integrated framework would increase the system complexity, a bootstrapped deep Q-Network (DQN) is employed to enhance the deep exploration of the reinforcement learning method and achieve adaptive decision making of AVs. Moreover, to make AV behavior understandable by surrounding HDVs to prevent unexpected operations caused by misinterpretations, we derive an inverse reinforcement learning (IRL) approach to learn the reward function of skilled drivers for the path planning of lane-changing maneuvers. Such a design enables AVs to achieve a human-like tradeoff between multi-performance requirements. Simulations demonstrate that the proposed integrated framework can guide AVs to take safe actions while guaranteeing high-speed cruising performance.

Spiking neural network (SNN)-based compute-in-memory (CIM) accelerator provides a prospective implementation for intelligent edge devices with higher energy efficiency compared with artificial neural networks (ANN) deployed on conventional Von Neumann architectures. However, the costly circuit implementation of biological neurons and the immature training algorithm of discrete-pulse networks hinder efficient hardware implementation and high recognition rate. In this work, we present a 40nm RRAM CIM macro (Tempo-CIM) with charge-pump-based leaky-integrate-and-fire (LIF) neurons and split-train-merged-inference algorithm for efficient SNN acceleration with improved accuracy. The single-spike latency coding is employed to reduce the number of pulses in each time step. The voltage-type LIF neuron uses a charge pump structure to achieve efficient accumulation and thus reduce the requirement for large capacitance remarkably. The split-train-merged-inference algorithm is proposed to dynamically adjust the input of each neuron to alleviate the spike stall problem. The macro measures 0.084mm2 in a 40nm process with an energy efficiency of 68.51 TOPS/W and an area efficiency of 0.1956 TOPS/mm2 for 4b input and 8b weight. IEEE