Inter-vehicle Distance Research Articles

A Dual Learning Model for Vehicle Trajectory Prediction

Automated vehicles and advanced driver-assistance systems require an accurate prediction of future traffic scene states. The tendency in recent years has been to use deep learning approaches for accurate trajectory prediction but these approaches suffer from computational complexity, dependency on a specific environment/dataset, and lack of insight into vehicle interactions. In this paper, we aim to address these limitations by proposing a Dual Learning Model (DLM) using lane occupancy and risk maps for vehicle trajectory prediction. To understand the spatial interactions of road users, make the model independent of the environment, and consider inter-vehicle distances, we embed an Occupancy Map (OM) into the trajectory prediction model. We also utilise a traffic scene Risk Map (RM) to explicitly consider a comprehensive definition of risk based on Time-to-Collision in the traffic scene. These two features employed in the encoder-decoder architecture improve system accuracy with less complexity and provide insight into the interaction between all road users. The experiment has been conducted on two different naturalistic highway driving datasets (i.e., NGSIM and HighD) demonstrating algorithm independence from a single environment. Comparison results indicate that the DLM achieves a more accurate trajectory prediction with a less complex structure compared with existing approaches in terms of RMS prediction error, which indicates the effectiveness of DLM in such a context.

Cooperative Adaptive Cruise Control of Heterogeneous Vehicle Platoons

Abstract Road throughput can be increased by cooperative adaptive cruise control (CACC), which allows vehicles to drive at short inter-vehicle distances without compromising string stability by using wireless inter-vehicle communications. Practical application, however, may involve vehicles with different driveline dynamics, thus forming a heterogeneous platoon. This property potentially requires knowledge of other vehicle’s driveline dynamics for implementation of CACC, which may not be available. As opposed to robust or adaptive approaches, the heterogeneity problem is solved here by revisiting an existing, widely adopted controller for homogeneous strings using an input-output linearization approach. As a result, a class of controllers is obtained which allows for vehicle strings that are heterogeneous with respect to driveline dynamics, without requiring knowledge of these dynamics. Furthermore, it is shown that the new controller represents a class of controllers that encompasses the original homogeneous controller. To illustrate the performance of the new controller, simulations of a heterogeneous platoon are presented and the string stability properties are assessed. From this analysis, it appears that the new controller performs at least as good as the original one, in terms of minimum string-stable time gap, settling time, and maximum jerk.

A Computationally Efficient Predictive Cruise Control for Automated Electric Vehicles

This paper proposes an energy-efficient predictive cruise control (PCC) system to cope with range anxiety of automated electric vehicles. The proposed approach is formulated as an optimal control problem to realize better energy efficiency and ensure safe inter-vehicle distance. To improve computational efficiency, a fast algorithm combining Gauss pseudospectral method (GPM) and moving horizon control (MHC) is introduced to solve this nonlinear optimal problem. The comparative simulation results reveal that the energy economy of the PCC system is improved about 4.1%, and its computation time is reduced compared with the Euler method while ensuing the same accuracy.

Combined Cooperative Adaptive Cruise Control using Collective Initial Excitation based Distributed Parameter Estimator

In cooperative adaptive cruise control (CACC), autonomous vehicles are grouped into a string of platoon and, the main objective is to automatically adapt their speed using on-board sensors and communication with the preceding vehicle to maintain a desired inter-vehicle distance. Cruise control is achieved in the presence of parametric uncertainty in the vehicle dynamics using principles of adaptive control. This work proposes a novel combined CACC strategy for an uncertain homogeneous platoon with guaranteed parameter convergence and asymptotic string stability. A novel distributed consensus-based parameter estimator is proposed in conjunction with a model reference adaptive control (MRAC) algorithm using a direct control-gain update law. The algorithm ensures exponential parameter estimation error convergence to zero as well as asymptotic convergence of tracking-error to zero. Conventional CACC protocols require a condition of persistence of excitation (PE) for parameter convergence, which is required for better transient performance in converging to a string stable configuration. The PE condition is highly restrictive in the context of cruise control since velocity profiles which are demanded in the platoon model do not typically satisfy the PE condition. In contrast, the proposed scheme can ensure parameter convergence under a significantly milder condition, coined as collective initial excitation (C-IE). The C-IE condition is an extension of the concept of initial excitation (IE), which is recently proposed in the context of adaptive control of single agent system. Unlike IE, the C-IE condition caters to distributed estimation in the context of multi-agent systems. As far as the authors are aware, this is the first work on CACC framework, which ensures exponential convergence of parameter estimation error of each vehicle under the mild condition of C-IE, which further leads to asymptotic convergence of the entire vehicle platoon to a string stable configuration. Simulation study dictates that the proposed CACC architecture outperforms the existing CACC algorithms in terms of tracking and estimation performance.

Bayesian Cooperative Localization With NLOS and Malicious Vehicle Detection in GNSS-Challenged Environments

Owing to the inter-vehicle non-line-of-sight (NLOS) measurement and malicious attack in global navigation satellite system (GNSS) challenged environment, the vehicle position precision is seriously damaged. In order to improve the vehicle position accuracy, we propose a new Bayesian cooperative localization scheme which tackles this problem by combining the vehicle position measurements and inter-vehicle distance measurements. In the proposed scheme, an abnormal vehicle detection algorithm (AVDA) is presented to eliminate the impacts of NLOS and malicious attack. Simulation results demonstrate that the proposed scheme can achieve excellent localization performance in the presence of NLOS and malicious attacks. Based on these results, the abnormal and normal detection rates of AVDA are approximate and the root mean square error (RMSE) is reduced to the sub-meter level. The performances of the proposed scheme are also verified in real environmental conditions by using the simulation of urban mobility (SUMO).

Packet Inter-Reception Time Conditional Density Estimation Based on Surrounding Traffic Distribution

Cooperation is an enabler for autonomous vehicles. A promising application of cooperative driving is high-density platooning, where trucks drive with low inter-vehicle distances. It aims at increasing the road and fuel efficiency whilst guaranteeing safety. The safe and efficient coordination of the control requires the regular and reliable exchange of V2V messages. The performance of the vehicular application has been shown to be strongly affected by the variation of the performances of the communications system. To be able to adapt their functional settings to these variations, vehicles need the ability to predict it. We present a prediction model for the packet inter-reception time platoon messages in an IEEE 802.11p network. This performance indicator is the subject of extensive research as it captures the irregularity of input for the control loop. The prediction model uses conditional density estimation based on the exponential distribution. We fit this model using a multi-layer perceptron regressor based on features representing the surrounding communication environment. The presented results are based on data collected during a full scale platooning simulations using ns-3 and SUMO. We compare different environment abstraction models and show the potential of on-line learning.

Disturbance observer approach for fuel-efficient heavy-duty vehicle platooning

ABSTRACTHeavy-duty vehicle platooning has received much attention as a method to reduce fuel consumption by keeping inter-vehicle distance short. When a platoon follows a fuel-optimal velocity profile calculated using preview road slope information, significant improvement in the fuel economy occurs. To calculate the optimal velocity in the existing method, however, platoon should acquire expensive road slope data in advance. As an alternative, we propose a road slope estimation method, which enables platoon to calculate the optimal velocity profile without the usage of actual road slope data. Other major challenges in platoon operation include overcoming the effect of the vehicle model uncertainties and external disturbances for ensuring the control performance. The most significant part of the disturbances arises from slopes along a route. Existing method for reducing the effect of the slope employs a feed-forward type compensation in the control loop by combining the vehicle position acquired from GPS and the slope database. However, this method exhibits limitations: the mass of the vehicles in the platoon is uncertain which lowers the accuracy of the feed-forward compensation, and the platoon requires the pre-acquired slope database. To overcome these limitations, we propose an alternative method employing disturbance observer. Simulations of various scenarios are conducted to show the efficacy of the proposed method using the actual road slope data of a Swedish highway.

Car Wake Flows and Ultrafine Particle Dispersion: From Experiments to Modelling

Improving air quality in urban environments and transportation systems is crucial. Concerns are related to health and environmental issues associated with huge costs. Car cabin is a microenvironment where pollutants can accumulate with possible risks for occupants. In automotive engineering, it has then become mandatory to study the path and dispersion of such pollutants emitted from the tailpipe of a car. In the present paper, the relation between the flow topology and the dispersion of ultrafine particles (UFP) in the wake of a vehicle is discussed. Experiments were undertaken at a reduced scale using simplified car models. Experimental conditions were defined to be representative of a vehicle in an urban environment. Based on experimental data, a simplified analytical model is developed, which aims at describing the concentration fields of UFP in the wake of a single vehicle for different rear slant angles. The strengths and limits of the present model are discussed and ways of improvements are suggested. Additional experiments are presented to assess the influence of the inter-vehicle distance on this recirculation region. Critical inter-vehicle distances were determined based on defined criteria for different rear slant angles of the leading vehicle and compared to safety clearances.

Compensation of Communication Delays in a Cooperative ACC System

Cooperative adaptive cruise control (CACC) employs intervehicle wireless communications to safely drive at short intervehicle distances, which improves road throughput. The underlying technical requirement to achieve this benefit is formulated by the notion of string stability, requiring the attenuation of the effects of disturbances in upstream direction. The wireless communication delay, however, significantly compromises string stability. In order to compensate for time delays and thus reduce the minimum string-stable time gap, a Smith predictor can be applied. For application of a Smith predictor, the time delay needs to be in a series connection with the plant to be controlled, which is realized by introducing a master-slave architecture for CACC. As a result, information exchange appears to become bidirectional, while the control scheme still follows the one-vehicle look-ahead strategy. Feasibility of both the master-slave CACC strategy and the Smith predictor is explicitly analyzed. With the proposed control scheme, the minimum string-stable time gap can be significantly decreased, even considering communication delay uncertainty. The results are validated using simulations with a platoon of CACC-equipped vehicles.

Analysis and design on intervehicle distance control of autonomous vehicle platoons

This paper presents an intervehicle distance control (IDC) to solve the problem of autonomous vehicle platooning, motivated by future automated highway system (AHS) or smart road which is proposed as intelligent transportation system (ITS) technology. First the velocity and position control of the single vehicle is studied based on internal model compensator. And then the platooning problem on multiple vehicles is solved in the light of multiagent concept. Moreover, the platoon condition is derived for the corresponding scheme. Further we analyze the influence of controller parameters on the whole system, and propose the guidance for parameter design. Finally some simulations are used to verify the effectiveness of the proposed IDC scheme with an analysis on controller parameters.

Stochastic geometry of network of randomly distributed moving vehicles on a highway

SummaryVehicular ad hoc networks (VANETs) have become an extensively studied topic in contemporary research. One of the fundamental problems that has arisen in such research is understanding the network statistical properties, such as the cluster number distribution and the cluster size distribution. In this paper, we analyze these characteristics in the case in which vehicles are located on a straight road. Assuming the Rayleigh fading model and a probabilistic model of intervehicle distance, we derive probabilistic distributions of the aforementioned connectivity characteristics, as well as distributions of the biggest cluster and the number of disconnected vehicles. All of the results are confirmed by simulations carried out for the realistic values of parameters.

The Meta Distribution of the SIR in Linear Motorway VANETs

The meta distribution of the signal-to-interference-ratio (SIR) is an important performance indicator for wireless networks because, for ergodic point processes, it describes the fraction of scheduled links that achieve certain reliability, conditionally on the point process. The calculation of the moments of the meta distribution requires the probability generating functional (PGFL) of the point process. In vehicular ad hoc networks (VANETs) along high-speed motorways, the Poisson point process (PPP) is a poor deployment model, because the drivers, due to the high speeds, maintain large safety distances. In this paper, we model the distribution of inter-vehicle distance equal to the sum of a constant hardcore distance and an exponentially distributed random variable. We design a novel \emph{discretization model} for the locations of vehicles which can be used to approximate well the PGFL due to the hardcore point process and the meta distribution of the SIR generated from synthetic motorway traces. On the other hand, the PPP overestimates significantly the coefficient-of-variation of the meta distribution due to the hardcore process, and its predictions fail. In addition, we show that the calculation of the meta distribution becomes especially meaningful in the upper tail of the SIR distribution.

Traffic-Centric Mesoscopic Analysis of Connectivity in VANETs

Abstract Vehicular ad hoc networks (VANETs) have emerged as an appropriate class of information propagation technology promising to link us even while moving at high speeds. In VANETs, a piece of information propagates through consecutive connections. In the most previous vehicular connectivity analysis, the provided probability density function of intervehicle distance throughout the wide variety of steady-state traffic flow conditions is surprisingly invariant. But, using a constant assumption, generates approximate communication results, prevents us from improving the performance of the current solutions and impedes designing the new applications on VANETs. Hence, in this paper, a mesoscopic vehicular mobility model in a multilane highway with a steady-state traffic flow condition is adopted. To model a traffic-centric distribution for the spatial per-hop progress and the expected spatial per-hop progress, different intervehicle distance distributions are utilized. Moreover, the expected number of hops, distribution of the number of successful multihop forwarding, the expected time delay and the expected connectivity distance are mathematically investigated. Finally, to model the distribution of the connectivity distances, a set of simplistic closed-form traffic-centric equations is proposed. The accuracy of the proposed model is confirmed using an event-based network simulator as well as a road traffic simulator.

Empirical Performance Models of MAC Protocols for Cooperative Platooning Applications

Vehicular ad-hoc networks (VANET) enable vehicles to exchange information on traffic conditions, dynamic status and localization, to enhance road safety and transportation efficiency. A typical VANET application is platooning, which can take advantage of exchanging information on speed, heading and position to allow shorter inter-vehicle distances without compromising safety. However, the platooning performance depends drastically on the quality of the communication channel, which in turn is highly influenced by the medium access control protocol (MAC). Currently, VANETs use the IEEE 802.11p MAC, which follows a carrier sense multiple access with collision avoidance (CSMA/CA) policy that is prone to collisions and degrades significantly with network load. This has led to recent proposals for a time-division multiple access (TDMA)-based MAC that synchronize vehicles’ beacons to prevent or reduce collisions. In this paper, we take CSMA/CA and two TDMA-based overlay protocols, i.e., deployed over CSMA/CA, namely PLEXE-slotted and RA-TDMAp, and carry out extensive simulations with varying platoon sizes, number of occupied lanes and transmit power to deduce empirical models that provide estimates of average number of collisions per second and average busy time ratio. In particular, we show that these estimates can be obtained from observing the number of radio-frequency (RF) neighbours, i.e., number of distinct sources of the packets received by each vehicle per time unit. These estimates can enhance the online adaptation of distributed applications, particularly platooning control, to varying conditions of the communication channel.

A Robust Active Safety Enhancement Strategy With Learning Mechanism in Vehicular Networks

Driving safety has been a hot topic in recent vehicular research. However, research on active control strategy, by which an accident might be avoided before it really happens, is still lacking, especially those appealing to machine learning methods with real traffic data. In addition, previous works constructed models with only one or a few factors considered, while the impact of multiple factors on a collision probability is overlooked. In this paper, based on machine learning methods with an actual traffic dataset, we propose a multi-level active safety control strategy taking the Multi-source, Multi-parameter, and Multi-purpose (3M) properties of an accident into consideration. First, by analyzing the impact of different conditions on an accident with the AHP (Analytic Hierarchy Process)-Ridge regression and bisecting K-means clustering model, the safety inter-vehicle distance is derived by learning from an actual traffic dataset. Besides, ELM(Extreme Learning Machines) is adopted as a verification scheme for safety distance calculation. Subsequently, we design a three-level active safety control scheme using the LQG (Linear Quadratic Gaussian) optimal-control model based on the obtained safety inter-vehicle distance. Numerical results show that by comparing with some classical braking and car-following models, our strategy can always keep the distance of two followed vehicles at a safety state. To further explore the impact of the time complexity on the rear-end collisions, we also implemented a road-test and verified that our model can timely respond to the risks and keep two cars always in safety.

A Predictive Framework for Dynamic Heavy-Duty Vehicle Platoon Coordination

This article describes a system to facilitate dynamic en route formation of heavy-duty vehicle platoons with the goal of reducing fuel consumption. Safe vehicle platooning is a maturing technology that leverages modern sensor, control, and communication technology to automatically regulate the inter-vehicle distances. Truck platooning has been shown to reduce fuel consumption through slipstreaming by up to 10%; under realistic highway-driving conditions. To further benefit from this technology, a platoon coordinator is proposed, which interfaces with fleet management systems and suggests how platoons can be formed in a fuel-efficient manner over a large region. The coordinator frequently updates the plans to react to newly available information. This way, it requires a minimum of customization with respect to the logistic operations. We discuss the system architecture in detail and introduce important underlying methodological foundations. Plans are derived in computationally tractable stages optimizing fuel savings from platooning. The effectiveness of this approach is verified in a simulation study. It shows that the coordinated platooning system can improve over spontaneously occurring platooning even under the presence of disturbances. A real demonstrator has also been developed. We present data from an experiment in which three vehicles were coordinated to form a platoon on public highways under normal traffic conditions. It demonstrates the feasibility of coordinated en route platoon formation with current communication and on-board technology. Simulations and experiments support that the proposed system is technically feasible and a potential solution to the problem of using vehicle platooning in an operational context.

Ecological Adaptive Cruise Control for Vehicles With Step-Gear Transmission Based on Reinforcement Learning

In this paper an ecological adaptive cruise controller to reduce the fuel consumption and ensure the safe inter-vehicle distance for vehicles with step-gear transmissions is presented. An optimal control strategy using reinforcement learning with a novel actor-gear-critic architecture is proposed to obtain the continuous traction force trajectory and the discrete gear shift schedule. The traction force is determined from an actor network to maintain a desired inter-vehicle distance which improves the driving safety in a car-following process. The gear shift schedule is derived from a gear network to reduce the fuel consumption. The control strategy is model-free and allows continuous online learning for different driving situations without look-ahead velocity information. Particularly the nonlinear vehicle dynamics, the nonlinear transmission efficiency map for different gear ratios, and the nonlinear fuel consumption map are learned for fuel consumption reduction. The proposed controller is evaluated for different driving scenarios to demonstrate its robustness. Furthermore simulation comparisons for different gear shift schedules and velocity trajectories are given underling the advantages in terms of fuel economy and driving safety.

Joint Communication and Control for Wireless Autonomous Vehicular Platoon Systems

Autonomous vehicular platoons will play an important role in improving on-road safety in tomorrow’s smart cities. Vehicles in an autonomous platoon can exploit vehicle-to-vehicle (V2V) communications to collect environmental information so as to maintain the target velocity and inter-vehicle distance. However, due to the uncertainty of the wireless channel, V2V communications within a platoon will experience a wireless system delay. Such system delay can impair the vehicles’ ability to stabilize their velocity and distances within their platoon. In this paper, the problem of integrated communication and control system is studied for wireless connected autonomous vehicular platoons. In particular, a novel framework is proposed for optimizing a platoon’s operation while jointly taking into account the delay of the wireless V2V network and the stability of the vehicle’s control system. First, stability analysis for the control system is performed and the maximum wireless system delay requirements which can prevent the instability of the control system are derived. Then, delay analysis is conducted to determine the end-to-end delay, including queuing, processing, and transmission delay for the V2V link in the wireless network. Subsequently, using the derived wireless delay, a lower bound and an approximated expression of the reliability for the wireless system, defined as the probability that the wireless system meets the control system’s delay needs, are derived. Then, the parameters of the control system are optimized in a way to maximize the derived wireless system reliability. Simulation results corroborate the analytical derivations and study the impact of parameters, such as the packet size and the platoon size, on the reliability performance of the vehicular platoon. More importantly, the simulation results shed light on the benefits of integrating control system and wireless network design while providing guidelines for designing an autonomous platoon so as to realize the required wireless network reliability and control system stability.

Ecological Adaptive Cruise Control and Energy Management Strategy for Hybrid Electric Vehicles Based on Heuristic Dynamic Programming

In this paper, an ecological adaptive cruise controller (ECO-ACC) for parallel hybrid electric vehicles (HEVs) in a car-following scenario is presented to improve the fuel economy and to maintain a desired inter-vehicle distance from the preceding vehicle. An ACC based on action dependent heuristic dynamic programming (ADHDP) is proposed to obtain an ecological velocity profile and realize an active distance control in normal driving situations. ADHDP is able to adapt internal parameters online and can thus deal with systems with disturbances. Furthermore, an adaptive energy management strategy for HEVs is introduced to control the gear shift and power split for fuel consumption optimization. The gear shift command is designed by enumeration, and the power distribution between the engine and the electric motor is performed by ADHDP. The developed ACC and energy management strategy are finally combined to an ECO-ACC to achieve a multi-objective optimization. Only the current velocity and acceleration of the preceding vehicle are used while knowledge about the future velocity is not needed. The simulations of different driving cycles indicate that the ECO-ACC can lead to near-optimal fuel economy and comfortable driving.

Roles of individual differences and traffic environment factors on children’s street-crossing behaviour in a VR environment

ObjectivePedestrian injuries are among the most common cause of death and serious injury to children. A range of risk factors, including individual differences and traffic environment factors, has been investigated...