Cooperative Adaptive Cruise Control: A Gated Recurrent Unit Approach

Embedded artificial intelligence solutions are promising controllers for future sustainable and automated road vehicles. This study presents a deep learning-based approach combined with vehicle communication technology for the design of a real-time cooperative adaptive cruise control (CACC). A particular type of recurrent neural network has been selected, namely a gated recurrent unit (GRU). GRU exhibits improved learning performance in control problems such as the CACC since it avoids the vanishing gradient problems that characterize long time series. A GRU has been trained using ad-hoc CACC datasets build-up according to an optimal control policy, i.e. dynamic programming (DP), for a battery electric vehicle. In particular, DP optimizes the longitudinal speed trajectory of the Ego (Following) vehicle in CACC so to achieve energy savings and passenger comfort improvement. Results demonstrate that the Ego vehicle controlled by the trained GRU can achieve an eco-friendly driving in CACC without compromising passenger comfort and safety requirements. Unlike DP, GRU holds strong real-time potential. The performance of the proposed GRU approach for CACC is verified by benchmarking with the optimal performance obtained off-line using DP in several driving missions

Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving

Automated vehicles require efficient and safe planning to maneuver in uncertain environments. Largely this uncertainty is caused by other traffic participants, e.g., surrounding vehicles. Future motion of surrounding vehicles is often difficult to predict. Whereas robust control approaches achieve safe, yet conservative motion planning for automated vehicles, Stochastic Model Predictive Control (SMPC) provides efficient planning in the presence of uncertainty. Probabilistic constraints are applied to ensure that the maximal risk remains below a predefined level. However, safety cannot be ensured as probabilistic constraints may be violated, which is not acceptable for automated vehicles. Here, we propose an efficient trajectory planning framework with safety guarantees for automated vehicles. SMPC is applied to obtain efficient vehicle trajectories for a finite horizon. Based on the first optimized SMPC input, a guaranteed safe backup trajectory is planned, using reachable sets. The SMPC input is only applied to the vehicle if a safe backup solution can be found. If no new safe backup solution can be found, the previously calculated, still valid safe backup solution is applied instead of the SMPC solution. Recursive feasibility of the safe SMPC algorithm is proved. Highway simulations show the effectiveness of the proposed method regarding performance and safety

Energy and Emissions Conscious Optimal Following for Automated Vehicles with Diesel Powertrains

The emerging application of autonomous driving provides the benefit of eliminating the driver from the control loop, which offers opportunities for safety, energy saving and green house gas emissions reduction by adjusting the speed trajectory. The technological advances in sensing and computing make it realistic for the vehicle to obtain a preview information of its surrounding environment, and optimize its speed trajectory accordingly using predictive planning methods. Conventional speed following algorithms usually adopt an energy-centric perspective and improve fuel economy by means of reducing the power loss due to braking and operating the engine at its high fuel efficiency region. This could be a problem for diesel-powered vehicles, which rely on catalytic aftertreatment system to reduce overall emissions, as reduction efficiency drops significantly with a cold catalyst that would result from a smoother speed profile. In this work, control and constrained optimization techniques are deployed to understand the potential for and achieve concurrent reduction of fuel consumption and emissions. Trade-offs between fuel consumption and emissions are shown using results from a single objective optimal planning problem when the calculation is performed offline assuming full knowledge of the whole cycle. Results indicate a low aftertreatment temperature when energy-centric objectives are used, and this motivates the inclusion of temperature performance metric inside the optimization problem. An online optimal speed planner is then designed for concurrent treatment of energy and emissions, with a limited but accurate preview information. An objective function comprising an energy conscious term and an emissions conscious term is proposed based on its effectiveness of 1) concurrent reduction of fuel and emissions, 2) flexible balancing between the emphasis on fuel saving or emissions reduction based on performance requirements and 3) low computational complexity and ease of numerical treatment. Simulation results of the online optimal speed planner over multiple drive cycles are presented, and for the vehicle simulated in this work, concurrent reduction of fuel and emissions is demonstrated using a specific powertrain, when allowing flexible modification of the drive cycle. Hardware-in-the-loop experiment is also performed over the Federal Test Procedure (FTP) drive cycle, and shows up to 15% reduction in fuel consumption and 70% reduction in NOx emissions when allowing a flexible following distance. Finally, the stringent requirement of accurate preview information is relaxed by designing a robust re-formulation of the energy and emissions conscious speed planner. Improved fuel economy and emissions are shown while satisfying the constraints even in the presence of perturbations in the preview information. A Gaussian mixture regression-based speed prediction is applied to test the performance of the speed following strategy without assuming knowledge of the preview information. A performance degradation is observed in simulation results when using the predicted velocity compared with an accurate preview, but the speed planner preserves the capability to improve fuel and tailpipe emissions performance compared with a non-optimal controller.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/170004/1/huangchu_1.pd