The Controller Area Network with Flexible Data- Rate (CAN FD) is the new generation of CAN technology to meet the daily increasing bandwidth requirement for Automotive Cyber-Physical Systems (ACPS). However, ACPS is a security- critical system, an efficient security/timing-aware Design Space Exploration (DSE) method is required to fully utilize CAN FD's high data phase data-rate. In this paper, we propose an AUTOSAR-compliant system model that integrates both timing and security constraint, an integrated Mixed-Integer Linear Programming formulation (i-MILP) for the optimal DSE of CAN FD, and a divide-and-conquer approach to the i-MILP (dc-MILP) to address its timing complexity problem. The experiment results show that dc-MILP scales well for industrial-size systems, and saves 1.94% to 4.76% bandwidth utilization and guarantees the schedulability for more signal sets by comparing with state-of- the-art algorithm.

The Internet of Things plays an indispensable role in the development of connected vehicles, which will pave the way for road safety applications. In recent years, the concept of a cooperative collision warning system (CCWS) has been introduced and developed to enhance road safety, and it has been seen as a typical Internet-of-Vehicles application. In most CCWSs, it is vital to have a detection mechanism based on trajectory predictions where the uncertainties associated with vehicular state and motion are complex. However, most available approaches in this regard did not consider these uncertainties. Hence, this article proposes a new collision risk assessment (CRA) method where sigma trajectories that include multiple possible trajectories considering multiple aspects of vehicular motion are designed to cope with vehicular uncertainties. Our method is implemented in a novel server-based architecture, which is different from the commonly used vehicle-based controlled CCWSs. The CRA is provided as a service by a cloud server. The proposed method and architecture are validated and evaluated through extensive real-world experiments. Experimental results show that our method outperforms a referenced method in terms of CRA and achieves better robustness in tolerating communication delays and dropouts. Latencies in CRA service were analyzed, and it was found that powerful computing resources provided by cloud servers can significantly decrease computational cost, which will indirectly compensate for communication costs in the future. Based on our high-performance CRA method, the proposed architecture can be regarded as a novel option for CCWS design.

Various embedded sensors such as LiDAR, cameras and GPS are mounted on vehicles today. And, vehicles with communication equipment can exchange sensor data between not only surrounding vehicles but also road-side-units. To achieve traffic safety and comfort driving, utilization of these traffic data are important for autonomous driving and advanced driving assistance systems. Our research group has been developing a city-scale traffic data management platform named DynamicMap 2.0. This paper proposes a software architecture of our platform. Our platform uniformly treats road maps, static information, dynamic information and predicted information. Road maps consist of three-level granularities: links, lanes and physical shapes. Especially, a data representation of lane-level road maps is newly proposed. Static information includes fixed properties about objects on the map. Dynamic information includes streaming data from sensors. And, predicted information is computed by prediction algorithms and simulations. Our platform provides query language for searching these traffic data. A developer of traffic applications writes a query (continuous query or one-shot query) which specifies integration and filtering of data. As a use case of our platform, we developed a prototype of a cooperative merging assistance system at intersections, and verify that our platform operates correctly.

Extensibility is an important optimization objective for the E/E architecture of automotive cyber-physical systems (ACPS), while little attention has paid to the extensibility-aware design of in-vehicle network. To address this problem, this paper formulates a trade-off problem that balances the bandwidth utilization and the extensibility from the initial design of CAN FD. We firstly propose a new extensibility model and the related evaluation metric, and then two optimization algorithms, namely, the mixed integer linear programming (MILP) approach and the simulated annealing (SA) based heuristic approach, are proposed to resolve the trade-off problem for mid-sized and industry sized signal sets, respectively. The experiment results show the efficiency of the proposed extensibility metric and the optimization algorithms. By comparing with state-of-the-art algorithm, the MILP reduces the increase range of the bandwidth utilization of the extended signal set by 18.17% to 57.64% averagely, and 49.22% to 89.40% maximally, with only 0.06% to 0.79% bandwidth utilization overhead; the SA approach can reduces the increase range of the bandwidth utilization of the extended signal set by 12.71% to 58.33% averagely, and 40.08% to 89.40% maximally, with only 0.06% to 0.8% bandwidth utilization overhead.

To realize the optimized design of automotive cyber- physical system, it is required to consider the interplay between communication and computation. However, existing research about the design of CAN with flexible data-rate (CAN FD) ignores this, it only considers the minimization of bandwidth utilization and neglects the fact that it would trigger too many unnecessary message receiving interrupts (MRIs) on message receiving electronic control units (ECUs). To address this prob- lem, this paper formulates a trade-off problem that balances the bandwidth utilization and the number of unnecessary MRIs during the design of CAN FD. We firstly propose an algorithm to analyze the number of unnecessary MRIs triggered by the packed messages, and then two heuristic algorithms, namely, the Top-Down approach and the Hybrid approach, are proposed to resolve the trade-off problem for mid-sized and large signal sets, respectively. The experiment results show that compared with state-of-the-art algorithm, the Top-Down approach reduces the unnecessary MRIs by 10.48% to 99.89% with only 0.02% to 1.32% bandwidth utilization overhead, the Hybrid approach reduces the unnecessary MRIs by 23.15% to 99.63% with only 0.13% to 2.07% bandwidth utilization overhead.