V2V Wireless Communication Protocol for Rear-end Collision Avoidance on Highways with Stringent Propagation Delay

To notify all vehicles within a geographical area of an urgent situation, multi-hop communication is employed in a vehicular ad hoc network (VANET) to propagate the emergency warning messages. While being interfered by the existing periodic single-hop safety messages, the transmission of warning messages can also be strongly affected by the broadcast storm problem and the collisions due to hidden nodes. This paper develops an efficient tone-based time-slotted multi-hop protocol that significantly improves the performance of the emergency warning message transmissions. Specifically, we assign the responsibility of message forwarding to only a subset of vehicles on the road, known as ``segment leader," as part of our broadcast storm suppression strategy. To avoid the potential interference caused by the safety messages, we employ separate multi-hop time slots for the warning messages, which are reserved by a newly-proposed out-of-band long range CLEAR tone. Such a tone-based reservation mechanism allows the continuous transmission of the safety messages in the absence of a CLEAR tone, giving rise to a much higher network utilization. Our extensive simulation results confirm the timely and reliable delivery of the emergency warning messages while the required number of multi-hop transmissions is significantly reduced. At the same time, a high packet reception rate and a low end-to-end delay are maintained for the existing single-hop safety messages.

This paper presents a probabilistic decision-making framework for rear-end collision avoidance systems, focusing on modeling the impact of major collision-causing factors on the occurrence of accidents. Decisions on when and how to assist drivers are made using a Bayesian network approach according to collision risk evaluation results, given a prior probabilistic knowledge. The structure of the Bayesian network model is learnt using a K2 algorithm with a practical dataset. To provide adequate response time for drivers, we also predict collision probability in the next monitoring interval using a Kalman filter model. The prediction accuracy is evaluated with different use cases and compared to real scenarios with the obtained dataset. To make our framework more relevant to practical applications, we also discuss the corresponding safety control strategies by classifying collision risk into high and low levels. In addition, the proposed model is evaluated through experiments including simulations and road tests. In the simulations, the algorithms are tested in different scenarios with various configurations of weather conditions, driver response capability, and vehicular dynamics. In order to demonstrate collision avoidance performance, the proposed model is also compared to existing schemes. In the road tests, the algorithm is embedded into an unmanned vehicle with predefined parameters to evaluate the impact of computational complexity on rear-end collision avoidance. Numerical results show that the proposed model provides an accurate estimation for car-following collision risk with a relatively low complexity, taking into account the impacts of vehicle dynamics, driver reaction capacity, and external environment on rear-end collisions.

A joint vehicle-vehicle/vehicle-roadside communication protocol is proposed for cooperative collision avoiding in Vehicular Ad Hoc Networks (VANETs). In this protocol, emergency warning messages are simultaneously transmitted via Vehicle-to-Vehicle (V2V) and Vehicle-to-Roadside (V2R) communications in order to achieve multipath diversity routing. In addition, to further improve communication reliability and achieve low latency, a Multi-Channel (MC) technique based on two nonoverlapping channels for Vehicle-Vehicle (V2V) and V2R (or R2V) is proposed. The simulation results demonstrate that the proposed joint V2V/V2R (R2V) communication protocol is capable of improving the message delivery ratio and obtaining low latency, which are very important merits for highway traffic safety.

The development of an intelligent connected monorail transit system offers an effective solution to the mismatch between passenger flow and system capacity at various time intervals within urban rail networks. As the core of such a system lies the virtual coupling (VC) technology, which dynamically adjusts train configurations in response to real-time passenger demand, thereby improving resource utilization. However, during VC operations, severe communication delays between vehicles or the sudden emergence of obstacles ahead may still result in rear-end collisions among coupled vehicles, posing significant safety risks. To address these challenges, this paper focuses on the active collision avoidance control of intelligent connected monorail vehicles operating within the VC environment. At the modeling level, a control model is developed to facilitate VC between leading and following vehicles, and the dynamic characteristics of typical operational scenarios—including station approach coupling, tracking coupling, and departure decoupling—are thoroughly analyzed. Building upon this foundation, the train’s behaviors under collision avoidance during accelerated departures, decelerated arrivals, and unexpected obstacle encounters are further investigated. In terms of control strategy, a Model Predictive Control (MPC) algorithm is introduced to enable efficient coordination and proactive collision avoidance among trains. Ultimately, a simulation platform based on Chongqing Rail Transit Line 3 is established for validating the proposed model and algorithm under representative operating scenarios. The evaluation demonstrates gains in system flexibility and safety and technical foundation for the practical implementation of intelligent rail transit systems.

IEEE 802.16e WiMAX is a promising new technology for broadband access networks. Amongst the class of applications that can be supported is real time video services (such as IPTV, broadcast of live events etc.). These applications are bandwidth hungry and have stringent delay constraints. Thus, scalable support for such applications is a challenging problem. To address this challenge, we consider a combination of approaches using multicast, layer encoded video and adaptive modulation of transmissions. Using these, we develop algorithms to ensure efficient, fair and timely delivery of video in WiMAX networks. The corresponding resource allocation problem is challenging because scheduling decisions (within a WiMAX base station) are performed in real-time across two dimensions, time and frequency. Moreover, combining layered video with appropriate modulation calls for novel MAC algorithms. We model the multicast resource allocation problem in WiMAX and demonstrate this problem to be NP-hard. We present a fast greedy algorithm that is (i) provably within a constant approximation of the optimal solution (based on a metric that reflects video quality as perceived by the user), and (ii) performs within 87-95% of the optimal as demonstrated by realistic simulations. We also demonstrate that our algorithm offers a 25% improvement over a naive algorithm. Moreover, in terms of the average rate received by each user, our algorithm out-performs the naive algorithm by more than 50%.

Rear-end collisions are one of the most common types of accidents on roads. Global Satellite Navigation Systems (GNSS) have recently become sufficiently flexible and cost-effective in order to have great potential for use in rear-end collision avoidance systems (CAS). Nevertheless, there are two main issues associated with current vehicle rear-end CAS: (1) achieving relative vehicle positioning and dynamic parameters with sufficiently high accuracy and (2) a reliable method to extract the car-following status from such information. This paper introduces a novel integrated algorithm for rear-end collision detection. Access to high accuracy positioning is enabled by GNSS, electronic compass, and lane information fusion with Cubature Kalman Filter (CKF). The judgment of the car-following status is based on the application of the Adaptive Neurofuzzy Inference System (ANFIS). The field test results show that the designed algorithm could effectively detect rear-end collisions with an accuracy of 99.61% and a false alarm rate of 5.26% in the 10 Hz output rate.

Unsafe rear-end collision avoidance in Alzheimer's disease

Development of rear-end collision avoidance system

In this paper, a rear-end collision control model is proposed using the fuzzy logic control scheme for the autonomous or cruising vehicles in Intelligent Transportation Systems (ITSs). Through detailed analysis of the car-following cases, our controller is established on some reasonable control rules. In addition, to refine the initialized fuzzy rules considering characteristics of the rear-end collisions, the genetic algorithm is introduced to reduce the computational complexity while maintaining accuracy. Numerical results indicate that our Genetic algorithm-optimized Fuzzy Logic Controller (GFLC) outperforms the traditional fuzzy logic controller in terms of better safety guarantee and higher traffic efficiency.

A comparison of experienced and novice drivers’ rear-end collision avoidance maneuvers under urgent decelerating events

Rear-end collision often leads to serious casualties and traffic congestion. The consequences are even worse for multiple-vehicle collision. Many previous works focused on collision warning and avoidance strategies of two consecutive vehicles based on onboard sensor detection only. This paper proposes a centralized control strategy for multiple vehicles to minimize the impact of multiple-vehicle collision based on vehicle-to-vehicle communication technique. The system is defined as a coupled group of vehicles with wireless communication capability and short following distances. The safety relationship can be represented as lower bound limit on deceleration of the first vehicle and upper bound on maximum deceleration of the last vehicle. The objective is to determine the desired deceleration for each vehicle such that the total impact energy is minimized at each time step. The impact energy is defined as the relative kinetic energy between a consecutive pair of vehicles (approaching only). Model predictive control (MPC) framework is used to formulate the problem to be constrained quadratic programming. Simulations show its effectiveness on collision mitigation. The developed algorithm has the potential to be used for progressive market penetration of connected vehicles in practice.

Mobile opportunistic networking is a promising technology that can supplement existing cellular and WiFi networks to provide desirable services for smart and connected communities. Message routing is the most compelling challenge in mobile opportunistic networks due to the lack of contemporaneous end-to-end paths and the resource constraints at mobile devices. To improve the probability of successful message delivery, most existing routing schemes use the past contact history to predict future contacts for message forwarding, and exploit message replication and redundancy for multicopy routing. However, most existing prediction-based routing schemes simply use the average pairwise contact probability as the routing metric and neglect the benefits of exploring fine-grained contact information such as pairwise repeated contact patterns to improve the accuracy of predicting future contacts. Moreover, there is no efficient mechanism that can adaptively control message replication in a decentralized manner to achieve both high probability of successful message delivery and low message overhead. To address these problems, we present FGAR, a routing protocol designed for mobile opportunistic networks by leveraging fine-grained contact characterization and adaptive message replication. In FGAR, contact history is characterized in a fine-grained manner with timing information using a sliding window mechanism, and future contacts are predicted based on the fine-grained contact information, thereby improving the accuracy of contact prediction. We further design an efficient message replication scheme in which message replication is controlled in a fully decentralized manner by taking into account the expected message delivery probability, the replication history, and the quality of the encountered device. A replica is generated only when it is necessary to fulfill the expected message delivery probability. We evaluate our scheme through trace-driven simulations, and the simulation results show that FGAR outperforms existing schemes. In comparison with PRoPHET, FGAR can achieve more than 20% improvement on average on successful message delivery, whereas the message overhead has been reduced by a factor up to 15.

Recently, wireless channel in vehicle-to-vehicle (V2V) communications is highly challenging due to rapid signal fluctuations, multi-path fading, and frequent obstructions. To mitigate these issues, this paper proposes a three-dimensional (3D) end-to-end channel model for reconfigurable intelligent surface (RIS)-assisted V2V wireless channels. The proposed channel model incorporates the motions of the mobile transmitter (MT), mobile receiver (MR), and a UAV-mounted RIS, capturing time-varying propagation characteristics. Key contributions include the derivation of exact analytical expressions for the level crossing rate (LCR) of the Rician fading envelope, along with efficient deterministic and stochastic simulation methods. Numerical results analyze the impact of RIS motion direction and scatterer distribution on the LCR, providing theoretical insights for optimizing RIS-assisted V2V systems in wireless communication environments.

The long propagation delay, limited bandwidth and high bit error rate pose great challenges in Media Access Control (MAC) protocol design for underwater acoustic networks. In this paper, we proposed an underwater MAC protocol based on Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA), which is called UW-CSMA/CA (CSMA/CA for underwater). Three areas of improvement are investigated, namely, the control rules, the deferral rules, and the backoff slot time. The simulations in ns2 demonstrate that the throughput performance of UW-CSMA/CA is clearly higher than that of original CSMA/CA protocol. We also conclude some regulations between throughput and network parameters, which provide basis of network optimization.

Unmanned Aerial Vehicles (UAVs), or drones, have recently become a favorable solution for fast parcel delivery due to their maneuverability and advances in navigation technologies. With the limitation of battery capacity and payload of drones, it is crucial to consider both efficiency and cost while conducting the tasks. Meanwhile, UAVs should not collide with each other while traveling to customers. In this article, we propose a UAV parcel delivery system involving deep reinforcement learning (DRL) approach for collision avoidance and a genetic algorithm for route optimization. Specifically, a delivery center generates near-optimal routes, loading UAV with parcels according to demands. Each UAV takes charge of delivering packages in compliance with the assigned route while avoiding collision with each other. We utilize DRL to achieve collision avoidance without having prior knowledge about the trajectories of other UAVs. Additionally, we adopt a genetic algorithm to obtain the lowest energy cost path for each UAV. To find such an optimized path, we solve a capacitated vehicle routing problem (CVRP) with a modified cost function and extra constraints. Realistic simulations using a physics engine and software-in-the-loop (SITL) are conducted to evaluate the feasibility of the proposed methods.