Real-time Autonomous Vehicle Research Articles

Retracted: GAP-MM: 5G-Enabled Real-Time Autonomous Vehicle Platoon Membership Management Based on Blockchain

Retracted: GAP-MM: 5G-Enabled Real-Time Autonomous Vehicle Platoon Membership Management Based on Blockchain

GAP-MM: 5G-Enabled Real-Time Autonomous Vehicle Platoon Membership Management Based on Blockchain

Autonomous Vehicle Platoon (AVP) is the most anticipated application of 5G ultrareliable and low latency communications. By joining the preexisting platoon to form the real-time AVP (RAVP), individual vehicles could gain benefits such as fuel consumption reduction and traffic safety enhancement. Unlike the scheduled AVP which only contains fixed vehicles, members in RAVP change frequently since individual vehicles would want to join or leave the platoon any time. Besides, malicious vehicles may attempt to sneak into the RAVP and try to manipulate the platoon. Public key cryptography and access control mechanisms can be adopted to create a relatively isolated area for communication inside the platoon. Nevertheless, there exist few works that take into account the regulation of the dynamic change members in RAVP. In this paper, we propose a membership management scheme for 5G-enabled RAVP by integrating revocable attribute-based encryption (RABE) and blockchain, namely, GAP-MM. It realizes fine-grained access control of key distribution and malicious vehicle’s key revocation efficiently. The sufficient evaluations and security analysis indicate that GAP-MM is practical for RAVP scenario in terms of both efficiency and security.

Real-Time Autonomous Vehicle Localization Based on Particle and Unscented Kalman Filters

In this paper, a real-time Monte Carlo localization (RT_MCL) method for autonomous cars is proposed. Unlike the other localization approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution. The RT_MCL method is based on the fusion of lidar and radar measurement data for object detection, a pole-like landmarks probabilistic map and a tailored particle filter for pose estimation. The lidar and radar are fused using the unscented Kalman filter (UKF) to provide pole-like static-object pose estimations that are well suited to serve as landmarks for vehicle localization in urban environments. These pose estimations are then clustered using the grid-based density-based spatial clustering of applications with noise algorithm to represent each pole landmark in the form of a source-point model to reduce computational cost and memory requirements. A reference map that includes pole landmarks is generated offline and extracted from a 3-D lidar to be used by a carefully designed particle filter for accurate ego-car localization. The particle filter is initialized by the fused GPS + IMU measurements and used an ego-car motion model to predict the states of the particles. The data association between the estimated landmarks by the UKF and that in the reference map is performed using the iterative closest point algorithm. The RT_MCL is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Extensive simulation studies have been carried out to evaluate the performance of the RT_MCL in both longitudinal localization and lateral localization.

Potentialities of Autonomous Vehicles for Online Monitoring of Motorway Traffic Volume

The fact that real-time autonomous vehicle (AV) traffic volume can be collected without a field detector by virtue of advanced global positioning system (GPS) and wireless communication technologies can render a promising solution to online monitoring of traffic volume in the upcoming AV era. To demonstrate this opportunity, this paper proposes a new method to monitor real-time motorway traffic volumes for road locations where no detector is installed using AV traffic volume. The modeling concept is based on the obvious fact that AV traffic volume is a direct portion of total traffic volume. The capabilities of the method are demonstrated through an experimental study using real-world GPS-enabled smartphone vehicle navigation data. The results show that online motorway traffic volume can be effectively monitored throughout the day with 5.69% average error at the 14.91% penetration rate of AVs during the daytime. Therefore, it is expected that AVs can at least be used as complementary means for the role of vehicle detectors in the near future due to the fact that the detection range of AVs is not spatially constrained.

IoT Based Real-time Autonomous Vehicle Tracking System

IoT Based Real-time Autonomous Vehicle Tracking System

A Testbed for Real-Time Autonomous Vehicle PHM and Contingency Management Applications

Autonomous unmanned vehicles are playing an increasingly important role in support of a wide variety of present and future critical missions. Due to the absence of timely pilot interaction and potential catastrophic consequence of unattended faults and failures, a real-time, onboard health and contingency management system is desired. This system would be capable of detecting and isolating faults, predicting fault progression and automatically reconfiguring the system to accommodate faults. This paper presents a robotic testbed that was developed for the purpose of developing and evaluating real-time PHM and Automated Contingency Management (ACM) techniques on autonomous vehicles. The testbed hardware is based on a Pioneer 3-AT robotic platform from Mobile Robots, Inc. and has been modified and enhanced to facilitate the simulations of select fault modes and mission-level applications. A hierarchical PHM-enabled ACM system is being developed and evaluated on the testbed to demonstrate the feasibility and benefit of using PHM information in vehicle control and mission reconfiguration. Several key software modules including a HyDE-based diagnosis reasoner, particle filtering-based prognosis server and a prognostics-enhanced mission planner are presented in this paper with illustrative experimental results. This testbed has been developed in hope of accelerating related technology development and raising the Technology readiness level(TRL)ofemergingACM techniques for autonomous vehicles.

A value-based scheduling approach for real-time autonomous vehicle control

In future real-time systems such as those required for intelligent autonomous vehicle control, we need flexibility in choosing the set of services to support under varying environmental conditions and system states. It is not feasible to make an optimal choice of services at run-time, so we propose a method of ranking the services pre-run-time, based on the ‘utility' of each service. This paper focuses on the problem of calculating a ‘value' for the utility of each service alternative. We show how to derive values systematically and rationally, using Measurement Theory and Decision Analysis. The approach relies on engineering judgement and data input by a domain expert. In the context of autonomous vehicles, we believe that such knowledge would be available, making ‘value-based scheduling' a feasible approach.