This paper proposes a new approach to detecting road boundary for generating high-definition (HD) maps of unstructured automated valet parking (UAVP) environments. The proposed framework presents a height moment tile to efficiently represent the height distribution of UAVP environments, a filtering scheme to remove both static and dynamic obstacles, a curb detection algorithm, and a road boundary expansion algorithm. Our approach is evaluated through experiments conducted at an outdoor parking lot in the Pusan National University. The results show that the proposed framework is able to detect accurate and efficient road boundary in UAVP environments.

A connected network of automated vehicles on roads can increase the driving safety of driverless vehicles (i.e., autonomous vehicles). The critical level of dangerous situations on the road while driving can be increased by the speed, orientation, and traffic density of the vehicles involved. Therefore, there is a need for a maneuvering mechanism that handles both the current driving vehicle and the oncoming vehicles headed toward an emergency zone (e.g., road hazard and road accident spot). In this paper, we present a context-aware navigation protocol (CNP) that enhances the safety of vehicles driving in urban roads. Firstly, CNP includes a collision avoidance module that builds on both vehicular networks and on-board sensors to track vehicles’ behaviors, and this module analyzes the driving risks to determine the necessary maneuvers in dangerous situations. Secondly, CNP establishes a collision mitigation strategy that limits the severity of collision damages in hazardous road during non-maneuverable scenarios. We conducted a theoretical analysis as well as extensive simulations to prove and evaluate the effectiveness of CNP. The results show that CNP can reduce communication overhead from a baseline scheme by up to 60% while the risk of road collisions is less than 5%.

Based on the end-to-end deep reinforcement learning framework, we present an integrated reward function (IRF) that improves the driving safety and efficiency of autonomous vehicles. Its longitudinal component employs an incremental slope incentive as well as the incentive/penalty of safety distance, while the lateral components exploits a symmetric piecewise-linear reward function to model the lateral and heading deviations from the lane centerline. The CARLA simulation results show that the IRF agent vehicle can achieve an excellent driving performance in both longitudinal and lateral directions.

Many vehicular and ITS applications require a highly accurate real-time vehicle position estimate for ensuring their safety and efficiency. The current limitation of vehicle positioning research is the lack of reliable position reference regardless of the surrounding environments. To mitigate this problem, we use the position estimate of three-dimensional simultaneous localization and mapping (3-D SLAM) as the ground truth. Then, we experimentally assess the positioning accuracy of three GPS receivers, i.e. the normal GPS, SBAS-GPS, and RTK- GPS, in two different geographical environments. From the numerical results, the RTK-GPS is shown to achieve the best positioning accuracy (with RMSE of 1.18 m) in the PNU campus with a good sky view, but surprisingly, its positioning accuracy in a high-rise apartment complex is the worst (with RMSE of 42.6 m) presumably due to the blocking and multi-path reflection of the GPS signals.

Beacon broadcast is an essential mechanism to improve the awareness of driving situations by broadcasting vehicle kinematics to its neighbor vehicles at a regular rate. However, the current design of beacon broadcast does not fulfill the awareness requirement of many emerging safety-critical applications. In this paper, we present the mobility-adaptive beacon broadcast (MAB2) scheme for guaranteed tracking of degree (GTD) K. In the GTD K, a neighbor vehicle can estimate the position of the sending vehicle within a fixed threshold, given that the former receives at least one of the K latest beacons of the latter. To aim this, we characterize the feasible kinematics of the sending vehicle in the subsequent time, address how the MAB2scheme defers a new beacon broadcast as long as the tracking condition meets, and present several rigorous proofs on the correctness and optimality of the MAB2scheme. Numerical results show that the MAB2scheme achieves an outstanding GTD-K tracking performance, in terms of the timeliness of beacon broadcast, the reliability of vehicle tracking, and the tracking accuracy, while minimizing the DSRC CCH congestion.

Due to the increasing number of vehicles in recent years, traffic congestion problem is a common issue for residents of metropolises. For a better understanding of traffic congestion, the analyzed data from big data technology can be provided as timeline information. However, a scalability problem would occur when we convert raw traffic data into the timeline information due to the volume and complexity of traffic data. In this paper, we present two MapReduce-based approaches which extract the timeline information of road traffic data. By utilizing the distribute processing strategy, we can resolve the scalability problem. We propose an iterative approach of MapReduce as a baseline approach and a single iteration approach as an efficient solution. The single iteration easily extended to support various and/or complex analytic queries by providing proper codes at a reducer. We validate experimentally our MapReduce-based approaches on real traffic dataset from a Busan Intelligent Transport System (ITS) center.

In vehicular localization, there is an increasing interest in the fusion algorithm between the relative local sensing estimate (LSE) of ranging sensors and the absolute remote sensing estimate (RSE) of a GPS receiver. A challenging issues of the fusion algorithm is to find a matched pair corresponding to the same vehicle between two sets of estimates. To aim this, we present a greedy data matching (GDM) that finds an optimal set of pairs, consisting of a sensing estimate and a GPS estimate, based on the closeness of these estimates. To represent the closeness in spatial domain, we employ the Mahalanobis distance metric based on the position and the speed estimates. To cope with the temporal randomness of these estimates, we also present a moving-average weighting factor which can provide a robustness to the random error of the estimates. From the numerical results, we show that the GDM outperforms the existing schemes in terms of the correct matching probability.

With the emergence of diverse group-based services, multiple multicast groups are likely to coexist in a single network, and users may subscribe to multiple groups simultaneously. However, the existing group key management (GKM) schemes, aiming to secure communication within a single group, are not suitable in multiple multicast group environments because of inefficient use of keys, and much larger rekeying overheads. In this paper, we propose a new GKM scheme for multiple multicast groups, called the master-key-encryption-based multiple group key management (MKE-MGKM) scheme. The MKE-MGKM scheme exploits asymmetric keys, i.e., a master key and multiple slave keys, which are generated from the proposed master key encryption (MKE) algorithm and is used for efficient distribution of the group key. It alleviates the rekeying overhead by using the asymmetry of the master and slave keys, i.e., even if one of the slave keys is updated, the remaining ones can still be unchanged by modifying only the master key. Through numerical analysis and simulations, it is shown that the MKE-MGKM scheme can reduce the storage overhead of a key distribution center (KDC) by 75 percent and the storage overhead of a user by up to 85 percent, and 60 percent of the communication overhead at most, compared to the existing schemes.

In many active safety applications, each vehicle must periodically disseminate a beacon message including the status information, such as position, speed, steering, etc., so that a neighbor vehicle can better perceive and predict the kinematics of the vehicle sending this message. However, a simple broadcasting of beacon message may lead to a low message reception as well as an excessive delay. To resolve this problem, we consider the requirements of an active safety application, a channel load and a vehicle mobility in a highway scenario of vehicular networks. Based on these requirements, we analyze the impact of the following three key parameters of the beacon dissemination on the performance of vehicular networks: beacon period, beacon transmission power, and contention window (CW) size. We first derive a beacon period which is inversely proportional to the vehicle speed. Next, we mathematically formulate the maximum beacon load to demonstrate the necessity of the transmission power control. We finally present an approximate closed-form solution of the optimal CW size that leads to the maximum throughput of beacon messages in vehicular networks.

Although lots of research efforts have focused on group key management (GKM) for secure multicast, existing GKM schemes are inadequate for vehicle communication (VC) systems since they incur unnecessary rekeying overhead without considering the characteristics of VC systems such as Vehicle-to-Infrastructure communications and a great number of high mobility vehicles. Therefore, we propose a GKM scheme, called RSU-based decentralized key management (RDKM), dedicated for the multicast services in the VC systems. The RDKM scheme significantly reduces the rekeying overhead through delegating a part of the key management functions to the road-side infrastructure units (RSUs) and through updating the key encryption keys (KEKs) within a RSU. The performance of the RDKM scheme is analyzed in terms of communication overhead and storage overhead each of which has a strong impact on the performance of GKM. Furthermore, we propose an optimization algorithm that minimizes the weighted sum of the communication and the storage overhead, called the GKM overhead (GKMO), by appropriately determining the design parameters. The numerical results from the extensive analysis demonstrate that the RDKM scheme outperforms the existing GKM schemes in terms of the GKMO.

Many RFID systems use dynamic slotted ALOHA to identify the label information of the RFID tags. One of the key problems in the RFID system is how to estimate the number of RFID tags up to the desired level of accuracy. In this paper, we present the framework of tag estimation algorithm for the collision-oblivious (CO) reader which can only decide whether the tag response is successful or not. Thus, the CO reader must rely on the success estimator to predict the RFID tag population. We propose two estimation algorithms to predict the number of RFID tags, named the memoryless success estimator (MSE) and the intersection-based success estimator (ISE). The MSE considers only the estimate obtained at the current inventory round, while the ISE finds an appropriate intersection interval of the existing estimates collected at every inventory round. Through the simulation results, we demonstrate that the ISE is a fast, accurate, and controllable estimator whose performance is close to that of the collision/idle estimators.

Blocking probability is the most widely used performance metric for the design and the dimensioning of wavelength-routed WDM networks. In this paper, we study the blocking performance of such networks under two heterogeneities: traffic heterogeneity and configuration heterogeneity. The former means that different sessions may have different kinds of traffic, and the latter that different nodes and links may have different wavelength conversion capabilities and different number of optical fibers in use, respectively. We also investigate how these heterogeneities impact on the blocking performance of such networks. To achieve these goals, we present a dedicated analytical model for each heterogeneity. First, we present two single-link models for estimating the arrival-point wavelength occupancy distribution on a link with heterogeneous traffic: the FP model and the RP model. Both models are based on the BPP/M/C/C model in which the first two moments of an arbitrary session are matched by those of a birth-death process whose arrival rate linearly varies with the average number of busy wavelengths occupied by its own calls. Next, we derive two recurrence formulas to estimate the number of free wavelengths on a multi-fiber link and the number of free wavelengths after limited-range wavelength conversion. We also present a comprehensive analytical framework for estimating the blocking performance of WDM networks in the presence of both heterogeneities. From the numerical results, we demonstrate that our model is highly accurate in evaluating the blocking probability for a wide range of the heterogeneities discussed above.

In this paper, we investigate the core-switch mapping (CSM) problem that optimally maps cores onto an NoC architecture such that either the energy consumption or the congestion is minimized. We propose a many-to-many core-switch mapping (mCSM) that allows a switch (core) to have multiple connections to its adjacent cores (switches). We also present decomposition methods that can obtain the suboptimal solutions with enhanced computational efficiency. Our work is the first to provide an exact mixed-integer linear programming (MILP) formulation for the complete CSM problems, including the optimal choice of core placements, switches for each core, and network interfaces for communication flows. Experiments with four random benchmarks show that 4:4 mCSM achieves 81.2% of energy savings and 2.5% of bandwidth savings compared with one-to-one mapping. They also show that, for one-to-one mapping, our optimal solutions obtained by the full MILP save 34.8% of energy consumption and 34.4% of bandwidth requirement compared with those from the existing algorithms.

We propose a heuristic wavelength routing algorithm for IP datagrams in WDM networks which operates in a distributed manner. We first present an efficient construction method for a loose virtual topology with a required connectivity property, which reserves a few wavelengths to cope with dynamic traffic demands properly. We then develop a high-speed distributed wavelength routing algorithm adaptive to dynamic traffic demands and derive the general bounds on average wavelength utilization in distributed wavelength routing algorithms. Finally, through simulation, it is shown that our algorithm is efficient enough to be used in distributed WDM networks in terms of the blocking performance, control traffic overhead, and computational complexity.