The vehicular edge computing is considered an enabling technology for intelligent and connected vehicles since the optimization of communication and computing on edge has a significant impact on driving safety and efficiency. In this paper, with the road traffic assignment to "proactively" reshape the spatiotemporal distribution of resource demands, we investigate the joint service migration and mobility optimization problem for vehicular edge computing. The goal is to meet the service delay requirements of vehicular edge computing with minimum migration cost and travel time. As service migration and mobility optimization are coupled, the joint scheduling problem suffers from the curse of dimensionality, which cannot be solved in real time by centralized algorithms. To this end, a multi-agent deep reinforcement learning (MADRL) algorithm is proposed to maximize the composite utility of communication, computing, and route planning in a distributed way. In the MADRL algorithm, a two-branch convolution based deep Q-network is constructed to coordinate migration action and routing action. Extensive experimental results show that the proposed algorithm is scalable and substantially reduces service delay, migration cost and travel time as compared with the existing baselines.

In recent years, IoV (Internet of Vehicles) has become one of the most active research fields in network and intelligent transportation system. As an open converged network, IoV plays an important role in solving various driving and traffic problems by advanced information and communications technology. We review the existing notions of IoV from different perspectives. Then, we provide our notion from a network point of view and propose a novel IoV architecture with four layers. Particularly, a novel layer named coordinative computing control layer is separated from the application layer. The novel layer is used for solving the coordinative computing and control problems for human-vehicle-environment. After summarizing the key technologies in IoV architecture, we construct a VV (Virtual Vehicle), which is an integrated image of driver and vehicle in networks. VVs can interact with each other in cyber space by providing traffic service and sharing sensing data coordinately, which can solve the communication bottleneck in physical space. Finally, an extended IoV architecture based on VVs is proposed.

Object detection in traffic scenes has attracted considerable attention from both academia and industry recently. Modern detectors achieve excellent performance under a simple constrained environment while performing poorly under the actual complex and open traffic environment. Therefore, the capability of adapting to new and unseen domains is a key factor for the large-scale application and proliferation of detectors in autonomous driving. To this end, this paper proposes a novel category-induced coarse-to-fine domain adaptation approach (C2FDA) for cross-domain object detection, which consists of three pivotal components: (1) Attention-induced coarse-grained alignment module (ACGA), which strengthens the distribution alignment across disparate domains within the foreground features in category-agnostic way by the minimax optimization between the domain classifier and the backbone feature extractor; (2) Attention-induced feature selection module, which assists the model to emphasize the crucial foreground features and enables the ACGA to focus on the relevant and discriminative foreground features, without being affected by the distribution of inconsequential background features; (3) Category-induced fine-grained alignment module (CFGA), which reduces the domain shift in category-aware way by minimizing the distance of centroids with the same category from different domains and maximizing that of centroids with disparate categories. We evaluate the performance of our approach in various source/target domain pairs and comprehensive results demonstrate that C2FDA significantly outperforms the state-of-the-art on multiple domain adaptation scenarios, i.e., the synthetic-to-real adaptation, the weather adaptation, and the cross camera adaptation.

Autonomous driving vehicle (ADV) that is ready to transform our society and economy, is in desperate need of precise positioning over itself as well as surrounding environments. However, it is still a challenging issue for ADV to retrieve real-time positioning knowledge over road participants and dynamic surrounding environments, due to unsatisfied perception accuracy caused by sparse observations and limited perception range. Cooperative perception, which advocates cooperatively disseminating perception data among vehicles, has the potential to overcome the above limitations. To this end, this article proposes a novel edge-assisted multi-vehicle perception system to enhance vehicles’ awareness over surrounding environments, which is termed as EdgeCooper. EdgeCooper first schedules vehicles to share complementarity-enhanced and redundancy-minimized raw sensor data with an edge server, using multi-hop cooperative 5G V2X communications. Then, EdgeCooper merges vehicles’ individual views to form a holistic view with a higher resolution, thus enhancing perception robustness and enlarging perception range. We formulate multi-vehicle multi-hop cooperative data sharing as a minimum cost flow problem with conflict, and further prove that there exists no polynomial-time approximation algorithm with a constant performance ratio unless P = NP. Furthermore, a two-dimension graph coloring algorithm with guaranteed performance is proposed to eliminate conflict. We evaluate EdgeCooper by building a comprehensive simulation platform through a joint manipulation of SUMO, CARLA, NS3, and PyTorch. The experiment results show that, compared to a single vehicle’s perception, EdgeCooper performs effective and efficient in enhancing vehicular awareness, e.g., extending up to 3.6 times detection range and improving perception accuracy by 20%.

With the significant advance of vehicle-to-everything (V2X) techniques, unsignalized intersection coordination has been widely recognized to facilitate the development of automated vehicles (AVs) for the intelligent transportation system. However, how to guarantee driving safety while improving the unsignalized intersection management efficiency is a challenging issue. In this paper, we investigate the collision-free and efficient V2X-enabled AV scheduling problem at unsignalized intersections. First, by dividing the intersection zone into different collision sections (CSs), we formulate the intersection collision-free model into an absolute value programming (AVP) problem, which is proved to be NP-hard. We consider both the non-platoon and platoon traffic scenarios, and unlike previous algorithms, which require to control all the AVs at each scheduling step with computational intractability, our scheduling algorithm can assign a feasible time for each arriving AV with low complexity. Further, we propose an alternately iterative descent method (AIDM) to solve the AVP problem by assigning the optimal entering time for each arriving AV. Through extensive simulations with various traffic data generated by SUMO, we demonstrate that our proposed AIDM algorithm can significantly enhance the scheduling performance in terms of passing delay and scheduling throughput. Even though the AIDM algorithm achieves the same level of transportation performances with the state-of-the-art algorithm, it advances dramatically in computational complexity and communication overhead, which is easier to be implemented in practice.