Merge Algorithms for Intelligent Vehicles

There is an increased concern towards the design and development of computer-controlled automotive applications to improve safety, reduce accidents, increase traffic flow, and enhance comfort for drivers. Automakers are trying to make vehicles more intelligent by embedding processors which can be used to implement Electronic and Control Software (ECS) for taking smart decisions on the road or assisting the driver in doing the same. These ECS applications are high-integrity, distributed and real-time in nature. Inter-Vehicle Communication and Road-Vehicle Communication (IVC/RVC) mechanisms will only add to this intelligence by enabling distributed implementation of these applications. Our work studies one such application, namely Automatic Merge Control System, which ensures safe vehicle maneuver in the region where two roads intersect. We have discussed two approaches for designing this system both aimed at minimizing the Driving-Time-To-Intersection (DTTI) of vehicles, subject to certain constraints for ensuring safety. We have (i) formulated this system as an optimization problem which can be solved using standard solvers and (ii) proposed an intuitive approach namely, Head of Lane (HoL) algorithm which incurs less Computational overhead compared to optimization formulation. Simulations carried out using Matlab and C++ demonstrate that the proposed approaches ensure safe vehicle maneuvering at intersection regions. In this ongoing work, we are implementing the system on robotic vehicular platforms built in our lab

Merge-by-Wire: Algorithms and System Support

Automakers are trying to make vehicles more intelligent and safe by embedding processors which can be used to implement ldquoby-wirerdquo applications for taking smart decisions on the road or assisting the driver in doing the same. Given this proliferation, there is a need to minimize the computing power required without affecting the performance and safety of the applications. The latter is especially important since these by-wire applications are distributed and real-time in nature and involve deadline bound computations on critical data gathered from the environment. These applications have stringent requirements on the freshness of data items and completion time of the tasks. Our work studies one such safety-related automotive application namely, automatic merge control (AMC) which ensures safe vehicle maneuver in the region where two or more roads intersect.As our contributions, we (i) propose two merge algorithms for amc: head of the lane (HoL) and all feasible sequences (AFS) (ii) demonstrate how DSRC-based wireless communication protocol can be leveraged for the development of AMC (iii) present a real-time approach towards designing AMC by integrating mode-change and real-time repository concepts for reducing the processing power requirements and (iv) provide a scheduling strategy to meet AMC taskspsila timing requirements. Simulations and implementation on robotic vehicular platforms demonstrate the advantages of using our approach for constructing merge-by-wire systems

Online Scheduling of Task Graphs on Hybrid Platforms

International audienceModern computing platforms commonly include accelerators. We target the problem of scheduling applications modeled as task graphs on hybrid platforms made of two types of resources, such as CPUs and GPUs. We consider that task graphs are uncovered dynamically, and that the scheduler has information only on the available tasks, i.e., tasks whose predecessors have all been completed. Each task can be processed by either a CPU or a GPU, and the corresponding processing times are known. Our study extends a previous 4 m/k-competitive online algorithm [2], where m is the number of CPUs and k the number of GPUs (m ≥ k). We prove that no online algorithm can have a competitive ratio smaller than m/k. We also study how adding flexibility on task processing, such as task migration or spoliation, or increasing the knowledge of the scheduler by providing it with information on the task graph, influences the lower bound. We provide a (2 m/k + 1)-competitive algorithm as well as a tunable combination of a system-oriented heuristic and a competitive algorithm; this combination performs well in practice and has a competitive ratio in Θ(m/k). Finally, simulations on different sets of task graphs illustrate how the instance properties impact the performance of the studied algorithms and show that our proposed tun-able algorithm performs the best among the online algorithms in almost all cases and has even performance close to an offline algorithm