The emergence of software-defined vehicles (SDVs), combined with autonomous driving technologies, has enabled a new era of vehicle computing (VC), where vehicles serve as a mobile computing platform. However, the interdisciplinary complexities of automotive systems and diverse technological requirements make developing applications for autonomous vehicles challenging. To simplify the development of applications running on SDVs, we propose a comprehensive suite of vehicle programming interfaces (VPIs). In this study, we rigorously explore the nuanced requirements for application development within the realm of VC, centering our analysis on the architectural intricacies of the Open Vehicular Data Analytics Platform (OpenVDAP). We then detail our creation of a comprehensive suite of standardized VPIs, spanning five critical categories: Hardware, Data, Computation, Service, and Management, to address these evolving programming requirements. To validate the design of VPIs, we conduct experiments using the indoor autonomous vehicle, Zebra, and develop the OpenVDAP prototype system. By comparing it with the industry-influential AUTOSAR interface, our VPIs demonstrate significant enhancements in programming efficiency, marking an important advancement in the field of SDV application development. We also show a case study and evaluate its performance. Our work highlights that VPIs significantly enhance the efficiency of developing applications on VC. They meet both current and future technological demands and propel the software-defined automotive industry toward a more interconnected and intelligent future.

Edge computing enabled Intelligent Road Network (EC-IRN) provides powerful and convenient computing services for vehicles and roadside sensing devices. The continuous emergence of transportation applications has caused a huge burden on roadside units (RSUs) equipped with edge servers in the Intelligent Road Network (IRN). Collaborative task scheduling among RSUs is an effective way to solve this problem. However, it is challenging to achieve collaborative scheduling among different RSUs in a completely decentralized environment. In this paper, we first model the interactions involved in task scheduling among distributed RSUs as a Markov game. Given that multi-agent deep reinforcement learning (MADRL) is a promising approach for the Markov game in decision optimization, we propose a collaborative task scheduling algorithm based on MADRL for EC-IRN, named CA-DTS, aiming to minimize the long-term average delay of tasks. To reduce the training costs caused by trial-and-error, CA-DTS specially designs a reward function and utilizes the distributed deployment and collective training architecture of counterfactual multi-agent policy gradient (COMA). To improve the stability of performance in large-scale environments, CA-DTS takes advantage of the action semantics network (ASN) to facilitate cooperation among multiple RSUs. The evaluation results of both the testbed and simulation demonstrate the effectiveness of our proposed algorithm. Compared with the baselines, CA-DTS can achieve convergence about 35% faster, and obtain average task delay that is lower by approximately 9.4%, 9.8%, and 6.7%, in different scenarios with varying numbers of RSUs, service types, and task arrival rates, respectively.