Low-speed Scenarios Research Articles

How do long combination vehicles perform in real traffic? A study using Naturalistic Driving Data

This paper evaluates the performance of two different types of long combination vehicles (A-double and DuoCAT) using naturalistic driving data across four scenarios: lane changes, manoeuvring through roundabouts, turning in intersections, and negotiating tight curves. Four different performance-based standards measures are used to assess the stability and tracking performance of the vehicles: rearward amplification, high-speed transient offtracking, low-speed swept path, and high-speed steady-state offtracking. Also, the steering reversal rate metric is employed to estimate the cognitive workload of the drivers in low-speed scenarios. In the majority of the identified cases of the four scenarios, both combination types have a good performance. The A-double shows slightly better stability in high-speed lane changes, while the DuoCAT has slightly better manoeuvrability at low-speed scenarios like roundabouts and intersections.

A Shared-Road-Rights Driving Strategy Based on Resolution Guidance for Right-of-Way Conflicts

In addressing the critical issue of right-of-way conflicts in mixed-traffic environments, this paper introduces a novel shared right-of-way driving strategy that encompasses two guiding frameworks for resolution. The first framework applies to active lane changing. Before lane changing occurs, this framework allocates the right of way for autonomous vehicles (AVs). Based on the allocated right of way, the AVs decide whether to send a request for a shared right of way to relevant vehicles. To enhance lane-changing comfort, the vehicle assesses whether the variance of roll and lateral acceleration exceeds a preset threshold, ultimately deciding whether to proceed with the lane change. The second framework pertains to passive lane changing. After detecting an obstacle, this framework allocates the right of way. The AVs calculate acceleration based on their speed and distance from the obstacle, using this information to determine whether to change lanes or decelerate in order to avoid the obstacle. If lane changing is chosen, further evaluation is necessary. Based on the allocated right of way, the AVs decide whether to request a shared right of way from relevant vehicles. To improve lane-changing comfort, the AVs compare the variance of roll and lateral acceleration with that of pitch and longitudinal acceleration, and then they decide whether to proceed with the lane change. The proposed strategy has been validated in various scenarios, including high-speed (105 km/h), low speed (13 km/h), and general scenarios with AVs and obstacles at a distance of 125 m. The results show that the strategy effectively functions in both high-speed and low-speed scenarios.

Investigating the Impact of Automated Vehicles on the Safety and Operation of Low-Speed Urban Networks

Automated vehicles (AVs) will appear on the road soon and will influence the properties of road traffic networks such as capacity and safety. While the impact of AV on traffic operations has been discussed extensively, most existing literature in this direction focuses on microscopic and mesoscopic levels. This study examines the effect of AVs on traffic flow operations and crash risks at a network scale. In addition, this study discusses the implications for designing and operating safe, low-speed urban road networks. Simulation experiments were carried out in a grid road network with AVs and human-driven vehicles operating in mixed flow conditions. To assess traffic performance, the macroscopic fundamental diagram (MFD) relationships were estimated, specifically focusing on speed-density correlations. From this analysis, it was possible to extract key traffic performance indices such as capacity and critical speed. Using time-to-collision surrogate safety measures, macroscopic safety diagrams were generated which associate the level of congestion with the potential crash conflicts among vehicles at an aggregated spatial scale. Utilizing this knowledge, a novel multiobjective optimization based on the NSGA-II algorithm was applied to identify the optimal trade-off between efficiency and safety. The presence of AVs was found to have a positive impact on the capacity, critical density, and average speed on a system level, even in low-speed scenarios. Moreover, AVs can result in increased critical density in the network, which suggests that the road system can serve more vehicles at its capacity, thus improving efficiency, while decreasing the number of conflicts. These findings are useful for both traffic planners and operators.

AOR: Adaptive opportunistic routing based on reinforcement learning for planetary surface exploration

Planetary surface exploration mobile ad-hoc networks (PSEMANET) show the characteristics of routing void caused by craters and electromagnetic interference, relatively high dynamics caused by node heterogeneity, and small node capacity caused by load constraints, which is the reason for the increase of communication delay and packet loss. Adaptive opportunistic routing based on reinforcement learning (AOR) is an opportunistic routing protocol that determines the forwarding node based on the current coordinates, queue length, and the number of neighbors. The full forwarding areas are used to avoid routing void, and the dual competition mechanism is designed to avoid the node hidden problem. The Q-value obtained by reinforcement learning is used to design the dynamic delay cost (DDC) so that nodes in the forwarding areas can compete to achieve adaptive path switching to the environment. Compared with representative proactive routing OLSR, reactive routing AODV, geographic routing GPSR and opportunistic routing BLR, AOR and AOR with memory(AOR-M) have high packet delivery ratio (PDR) and low end-to-end delay in planetary surface exploration scenarios implemented by our simulation system. And the AOR-M protocol shows the lowest expected end-to-end delay and highest channel utilization in both high and low speed scenarios. The result shows that the AOR-M provides efficient and robust routing in planetary surface exploration mobile ad-hoc networks with a complex environment, highly dynamic nodes, and performance constraints.

Air-to-Ground Channel Modeling and Performance Analysis for Cellular-Connected UAV Swarm

In this letter, we investigate the air-to-ground (A2G) channel model and transmission performance for a cellular-connected massive multiple-input multiple-output unmanned aerial vehicle (UAV) swarm system. First, we propose a spatially and temporally correlated A2G channel model that focuses on non-isotropic scattering, line-of-sight (LoS) propagation, and moving scatterers. Second, we derive a novel closed-form signal-to-interference-and-noise ratio expression for uplink transmission, taking into account the effect of channel aging with strong LoS. The results show that the frame length, pilot overhead, and UAV swarm scale can be pre-designed in a fine-grained manner depending on the propagation environment and transmission task. For example, for a low-speed scenario, a long frame can be configured due to the negligible channel aging effect. Moreover, more pilots may mitigate the damage of channel aging, but it is no better when a short frame is needed for transmission such as ultra-reliable and low-latency communications.

A Successive Linearization in Feasible Set Algorithm for Vehicle Motion Planning in Unstructured and Low-Speed Scenarios

Motion planning in unstructured and low-speed environments is a fundamental and difficult task for all mobile robotics. If we view motion planning as an optimization problem, the non-convex collision avoidance constraints and the nonlinear vehicle dynamic constraints make motion planning challenging and time-consuming. In this paper, we propose a Successive Linearization in Feasible Set (SLiFS) algorithm to address these two difficulties. SLiFS consists of two steps. The first step is to iteratively construct convex feasible sets around the current trajectory to approximate non-convex collision avoidance constraints. The second step is to successively linearize the nonlinear dynamic constraints along the current trajectory and further penalize them into the objective function to avoid infeasible linearized constraints, so that the current trajectory can be reshaped within the obtained convex feasible sets by iteratively solving the linearized optimization problem. The main innovation of SLiFS algorithm is that we consider the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> norm type penalty function in the second step. We find that the sparsity of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> norm might help to satisfy the robotic dynamic constraints by numerical experiments. Numerical testing results show that our proposed SLiFS algorithm has a high success rate to find feasible trajectories and costs much less time than the classical interior-point method.

Anti-dissipation pressure correction under low Mach numbers for Godunov-type schemes

An effective and unified framework, termed anti-dissipation pressure correction, is established to overcome the deterioration in the accuracy observed in Godunov-type schemes in low-speed scenarios. Based on the scale analysis of the nonlinear characteristic line relation, the deteriorated-accuracy source term is associated with the speed of sound and velocity difference term. Subsequently, the anti-dissipation pressure correction term is constructed by using the scaling Mach number function to rescale the error source term to be of the magnitude of the convective velocity and ensure a satisfactory accuracy. This scaling function is a polynomial of the local Mach number and restrained by the shock indicator. Results of the asymptotic analysis demonstrate that the anti-dissipation pressure correction helps recover the correct Ma2 scaling of the pressure fluctuations and damps the checkerboard modes, and this aspect is verified considering low Mach number flows over the NACA0012 airfoil. This novel framework can remarkably enhance the low Mach number performance and is also applicable to scenarios involving moderate or high Mach numbers. The performance of the approach is validated through numerical investigations across a wide range of Mach numbers.

Sparse Elitist Group Lasso Denoising in Frequency Domain for Bearing Fault Diagnosis

The fault-induced impulse responses of localized bearing fault are usually interfered by the background noise and other harmonic components. They are strongly coupled together and are hard to be separated. It is crucial to develop a fast and reliable method to extract the impulse-based feature for online bearing fault diagnosis in the industry application. In this article, we propose a new sparse elitist group lasso denoising (SEGLD) algorithm in frequency domain to detect the incipient impulse-based fault feature, which is free of utilizing the prior knowledge. We first reveal the sparse characteristics of the bearing fault signals in frequency domain. Then, a tailored denoising model is proposed. To obtain a satisfactory analytical stationary solution, the Douglas-Rachford splitting solver is employed for the denoising model. Moreover, we explore the relationship between the best regularization parameters, the periodic information and the normalization estimated noise of the rolling bearing fault signal. A rule of adaptively selecting the best regularization parameters is demonstrated. Finally, the robustness and effectiveness of the proposed SEGLD algorithm are profoundly verified by the numerical simulation and two evaluation experiments under the conditions of early fault stage and low speed scenario. Also, it is demonstrated that the proposed approach outperforms the state-of-the-art method for extracting the weak fault feature.

Adversarial Training-Based Hard Example Mining for Pedestrian Detection in Fish-Eye Images

Since fish-eye cameras are popular in the intelligent transportation systems, accurate pedestrian detection in fish-eye images becomes more and more critical in low-speed scenarios. Usually, big data based training is the key for detectors to handle the distortion problem in fish-eye images. Especially, hard examples are more important for detectors. They have more complex features and are hard to recognize. In conventional methods, fish-eye images are collected and labeled manually. These methods are expensive and labor-intense. More importantly, these methods are still hard to collect abundant hard examples since they are rare in reality. This work proposes the Distortion Generation Network, which generates generous fish-eye images automatically using only small samples. Moreover, the Adversarial Distortion Generation Network is proposed to mine hard examples via adversarial training. These hard examples benefit detectors to be more robust to seriously distorted objects in fish-eye images. Experiments with the ETH, the KITTI, the GM-ATCI and real fish-eye datasets demonstrate that the proposed methods achieve higher accuracy than conventional methods in pedestrian detection in fish-eye images.

AUTONOMOUS NETWORK SELECTION STRATEGY FOR TELECARDIOLOGY APPLICATION IN HETEROGENEOUS WIRELESS NETWORKS

Existing telecardiology systems are mostly relying on a high bandwidth wireless technology. However, in developing countries, the coverage of high bandwidth wireless network is still imperfect. Thus, the existing telecardiology systems are unable to guarantee users are always connected to the healthcare service provider at anywhere. To overcome this issue, an autonomous network selection strategy for telecardiology application in heterogeneous wireless networks is proposed. This strategy is aware of user velocity, network quality, and telecardiology service setting (e.g. image, vital signs, ECG, etc.). It performs handover from one network to another without disruption to the link. The simulation results show that the proposed strategy outperforms conventional bandwidth-based strategy in term of handover rate, ping-pong effect and handover failure. It has successfully reduced the handover rate up to 97%, eliminated the ping-pong effect and handover failure in both high and low speed scenarios.

Using Glass Mat Thermoplastic as Automotive Bumper’s Material to Enhance Pedestrian Safety

The usage of softer systems in automotive bumper is a growing trend currently especially to serve the pedestrians safety function. The term softer here does refer to the bumper system’s dynamic behavior rather than its material’s flexure or tension modules. However, the usage of such softer systems would raise issues of structural integrity of the bumper during crash. There is a strong drive currently to adopt materials such as glass mat thermoplastic (GMT), high-strength sheet molding compound (SMC) for the bumper material and plastic polypropylene (PEP) for the bumper holders [1, 2, 3] in this regard. While both the GMT and SMC do enhance the pedestrian safety condition, they both show plastic deformation at crash, even in low-speed scenarios [2, 3]. The PEP holders react only as shock absorbers and act like mechanical fuses to be destroyed in car crash, preventing the main bumper from being damaged [4]. In this paper, we propose a remedy for this problem by changing the common system that the GMT and SMC materials are usually fitted at. We propose coating the bumper beam with a Rubber padding layer that eliminates the plastic strain at low-speed crash. We also examine the behavior of the PEP during such crash scenarios. We present here the results of a low-speed head-on automotive-pedestrian crash simulation scenario for these material models, using the explicit dynamics finite element code LS-DYNA within ANSYS integration setting. A simplified parameterized finite element model of the Ford Crown Victoria car’s bumper form is used in several crash simulations that are carried out to test the validity of this modified bumper system. Based on the results of these tests, we show that, applying the Rubber coating material for the GMT and SMC bumper beams eliminates the plastic stains at low-speed crash.

Analysis of Medium-Speed Runway Exit Maneuvers

Recent studies seem to indicate that fuel savings can be realised if more e‐cient surface movements can be achieved through means other than the engines of an aircraft, or by optimising the scheduling of aircraft on the ground. All aspects of ground manoeuvring therefore need to be studied to understand the impact of such changes. This paper forms part of an ongoing project to study ground manoeuvres for difierent speed ranges. Previous studies were targeted towards low-speed scenarios, while the focus of this paper is towards medium-speed manoeuvres, and more speciflcally, runway exit manoeuvres. Different methods are considered for the calculation of steering angles and clearance distances pertaining to runway exit manoeuvres. Kinematic equations that were derived for towing analysis form the basis of the runway exit study, from which empirical formulas are derived for steering angle and clearance predictions. The results of the new empirical method compare very well with kinematic methods, as well as detailed dynamic model simulations. The empirical method can be used to great efiect during the early design phases of an aircraft programme for the prediction of steering angles and clearance distances, when very little data is available. The greatest advantage of the proposed method is that any aircraft conflguration or runway exit can be analysed.

The modeling of single-boat, mid-water trawl systems for fishing simulation

A mathematical model of single-boat, mid-water trawl system, together with a graphical interface have been created for training purposes. The model consists of separate components for the trawler and fishing gear. The trawler component considers the influence of wind and current and can be used to simulate trawlers in ordinary-speed and low-speed scenarios. The fishing gear component considers the towing warp, the otter board, and the fishing net. The model components are connected by coordinating boundary conditions. All trawling processes, including cruising, towing, and hauling and deploying the fishing net, can be simulated using this approach. Simulations have been undertaken based on a single-boat, mid-water trawler. The simulated trawler speed and turning circle under cruising conditions were compared to sea trials, and simulated trawl mouth height and trawl resistance to flume experiments. The hydrodynamic performance of a single-boat, mid-water trawl has been analyzed, showing that trends in fishing gear parameters together with towing speed and warp length are intuitively correct, but trawling speed is overestimated, which requires that the model component for the controllable pitch propeller be improved.