Dynamic programming-based multi-vehicle longitudinal trajectory optimization

Abstract

The current key trend of technological revolution seeks to impact the system through vehicle-based revolution. As population, economic growth and personal travel activities continue to increase, traffic congestion remains as an extremely challenging problem due to limited road capacity and limited budgets for expanding infrastructure. Therefore there is significant interest in intelligent vehicles profits connectivity that offer opportunities for reduced emissions and clean energy sources. A recently emerging technology, autonomous vehicles or automated vehicles (AV) are likely to create a revolutionary paradigm shift in the near future for real-time traffic system automation and control. AV technology is expected to provide a wide range of new opportunities for managing transportation networks, and also redefines what is tractable regarding full system-wide optimization through a tight integration among vehicles and system managers. One of the most obvious benefits of AV is to improve the productivity of drivers by enabling the drivers to do something else than driving. However, it is clear that the driving environment and driver-vehicle interactions are expected to change, with the introduction of connected automated vehicles (CAV). Jointly optimizing multi-vehicle trajectories is a critical task in the next-generation transportation system with autonomous and connected vehicles. Based on a space-time lattice, it is presented a set of integer programming and dynamic programming models for scheduling longitudinal trajectories, where the goal is to consider both system-wide safety and throughput requirements under supports of various communication technologies. Newell's simplified linear car following model is used to characterize interactions and collision avoidance between vehicles, and a control variable of time-dependent platoon-level reaction time is introduced in this study to reflect various degrees of vehicle-to-vehicle or vehicle-to infrastructure communication connectivity. By adjusting the lead vehicle's speed and platoon-level reaction time at each time step, the proposed optimization models could effectively control the complete set of trajectories in a platoon, along traffic backward propagation waves. This parsimonious multi-vehicle state representation sheds new lights on forming tight and adaptive vehicle platoons at a capacity bottleneck. It is examined the principle of optimality conditions and resulting computational complexity under different coupling conditions.