The application of autonomous vehicles (AVs) requires a safe and efficient decision-making approach for diverse and complex traffic environments. Most existing methodologies focus on specific scenarios or tasks, and are not sufficiently effective for real-world driving situations. This paper proposes a Safety Interval Reserve (SIR) model to quantify safety time margin, which is inspired by human driving behavior. Concurrently, a SIR network is developed to parametrically delineate the changes of SIR under dynamic traffic conditions. Consequently, a SIR network based decision-making approach (DMA-SIR) is designed to unify the macro path planning and micro behavior decision-making. The macro layer optimizes the global path with considering driving efficiency, while the micro layer generates local driving behavior to ensure safety during decision-making. The DMA-SIR facilitates multi-task management through a unified model and is grounded in motion mechanism. Besides, dataset validation demonstrates the anthropomorphic characteristics of DMA-SIR. As a result, it offers an interpretable method that effectively avoid the black box problem. Finally, extensive simulations experiments are conducted using 48 complex scenarios to verify the performance of DMA-SIR. The results show that DMA-SIR can efficiently handle multi-vehicle scenarios and generate driving trajectory with significant greater efficiency, safety and comfort compared to other methods.

This paper proposes a two-stage multi-lane unsignalized intersection cooperative control strategy based on mixed platoons (MICS-PF) in the mixed traffic environment of connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs). In the first stage, we propose a 0–1 integer programming model based on vehicles’ intersection movements and potential car-following costs to organize vehicles into mixed platoons led by CAVs. Meanwhile, we design a rule-based algorithm to coordinate the relative distribution of platoons. Then, the lane-changing and car-following models are integrated to ensure that the platoons organization plan can be completed in the limited functional zone. In the second stage, we design an improved minimum covering clique algorithm to generate a conflict-free passing order of platoons by considering the platoon-level conflict relation and the potential yield delay. The proposed strategy couples the process of organizing vehicles into mixed platoons to pass through the intersection. The optimal platoon organization enables CHVs to receive accurate guidance when entering the intersection and avoids the disorderly lane change at the upstream road sections. The mixed platoon control also allows vehicles to avoid stop-and-go, improve the capacity of approach roads, and prevent the intersection capacity from being restricted by the inefficient traffic at the approach road. The conflict-free coordination method of mixed platoons optimizes the throughput of intersections and broadens the usage conditions of unsignalized intersections. A series of simulation experiments verify the effectiveness of the strategy, and the applicable boundary conditions are obtained.

When traffic density on urban roads is relatively large, it is difficult for a large number of O-vehicles (O-vehicles) in front of emergency vehicles (E-vehicles) to give way in a short time, resulting in a delay of E-vehicles. This study aimed to design an automatic way-giving system based on vehicle-to-vehicle communication (V2V) in the internet of vehicles (IoV). Also, a control algorithm was proposed for the platoon to give way to the E-vehicle in the minimum time, the factors such as the traffic density, number of vehicles in the platoon, the initial state, and speed limit were considered in this paper. The algorithm firstly calculated the dynamic adjustment trajectory for the platoon to complete the lane change, the adjustment time, and the minimum distance between the E-vehicle and the platoon at the beginning of lane change, to guide the platoon while changing the lane. A simulator utility was developed to interface with the microscopic traffic simulation package Simulation of Urban Mobility (SUMO) by using the Python program. Lastly, a two-lane road segment was simulated to evaluate the effectiveness of the control algorithm of automatic way-giving system, and its performance is compared with conventional vehicle operation. The results demonstrated that the operating efficiency of E-vehicles can be improved by 12.65% and the operating efficiency of O-vehicles can only be decreased by 3.25% by the automatic way-giving system. Besides, the notification distance is a key factor in affecting the operating efficiency of E-vehicles and O-vehicles.

Because of their capacity-approaching performance and their complexity/latency advantages, spatially-coupled (SC) codes are among the most attractive error-correcting codes for use in modern dense data storage systems. SC codes are constructed by partitioning an underlying block code and coupling the partitioned components. Here, we focus on circulant-based SC codes. Recently, the optimal overlap (OO), circulant power optimizer (CPO) approach was introduced to construct high performance SC codes for additive white Gaussian noise (AWGN) and Flash channels. The OO stage operates on the protograph of the SC code to derive the optimal partitioning that minimizes the number of graphical objects that undermine the performance of SC codes under iterative decoding. Then, the CPO optimizes the circulant powers to further reduce this number. Since the nature of detrimental objects in the graph of a code critically depends on the characteristics of the channel of interest, extending the OO-CPO approach to construct SC codes for channels with intrinsic memory is not a straightforward task. In this paper, we tackle one relevant extension; we construct high performance SC codes for practical 1-D magnetic recording channels, i.e., partial-response (PR) channels. Via combinatorial techniques, we carefully build and solve the optimization problem of the OO partitioning, focusing on the objects of interest in the case of PR channels. Then, we customize the CPO to further reduce the number of these objects in the graph of the code. SC codes designed using the proposed OO-CPO approach for PR channels outperform prior state-of-the-art SC codes by up to around 3 orders of magnitude in frame error rate (FER) and 1.1 dB in signal-to-noise ratio (SNR). More intriguingly, our SC codes outperform structured block codes of the same length and rate by up to around 1.8 orders of magnitude in FER and 0.4 dB in SNR. The performance advantage of SC codes designed using the devised OO-CPO approach over block codes of the same parameters is not only pronounced in the error floor region, but also in the waterfall region.

Spatially-coupled (SC) LDPC codes have recently emerged as an excellent choice for error correction in modern data storage and communication systems due to their outstanding performance. It has long been known that irregular graph codes offer performance advantage over their regular counterparts. In this paper, we present a novel combinatorial framework for designing finite-length irregular SC LDPC codes. Our irregular SC codes have the desirable properties of regular SC codes thanks to their structure while offering significant performance benefits that come with the node degree irregularity. Coding constructions proposed in this work contribute to the existing portfolio of finite-length graph code designs.