IIIT Hyderabad Publications |
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Model Predictive Control for Autonomous driving in Complex Urban ScenariosAuthor: Raghu Ram Theerthala Theerthala Date: 2019-11-13 Report no: IIIT/TH/2019/115 Advisor:Madhava Krishna AbstractModel Predictive Control(MPC) is a powerful optimization strategy for feedback control which is being used increasingly for Motion planning in autonomous driving. However there are two key restrictions on using MPC. One is the requirement of high performance computational resources as it performs full optimization for every time step. The other is requirement of an accurate predictive model which is less variant to various initializations in multiple scenarios.In this thesis, We propose a new model predictive control formulation for autonomous driving. The novelty of our MPC stems from the following results. Firstly, we adopt an alternating minimization approach wherein linear velocities and angular accelerations are alternately optimized. We show that in contrast to the joint optimization, the alternating minimization exploits the structure of the problem better, which in turn translates to a reduction in computation time. Secondly, our MPC explicitly incorporates the time-dependent non-linear actuator dynamics that captures the transient response of the vehicle for a given commanded velocity. This added complexity improves the predictive component of MPC, resulting in an improved margin of inter-vehicle distance during complex urban scenarios like occlusion during overtaking, lane-change, etc. Although past works have also incorporated actuator dynamics within MPC, there have been very few attempts towards coupling actuator dynamics to collision avoidance constraints through the non-holonomic motion model of the vehicle and analyzing the resulting behavior. We use a high fidelity simulator to benchmark our actuator dynamics augmented MPC with other related approaches in terms of metrics like inter-vehicle distance, trajectory smoothness, and velocity overshoot. Planning frameworks for autonomous vehicles must be robust and computationally efficient for realtime realization. At the same time, they should accommodate the unpredictable behavior of the other participants and produce safe trajectories. In this thesis, we also present a computationally, the efficient hierarchical planning framework for autonomous vehicles. This framework can generate safe trajectories in complex driving scenarios, which are commonly encountered in urban traffic settings. The first level of the proposed framework constructs a Model Predictive Control(MPC) routine using an efficient difference of convex programming approach, that generates smooth and collision-free trajectories. The constraints on curvature and road boundaries are seamlessly integrated into this optimization routine. The second layer is mainly responsible for handling the unpredictable behaviors that are typically exhibited by the other participants of traffic. It is built along the lines of time scaled collision cone(TSCC) which optimize for the velocities along the trajectory to handle such disturbances. We additionally show that our framework maintains an optimal balance between temporal and path deviations while executing safe trajectories. To demonstrate the efficacy of the presented framework, we validated it in extensive simulations in different driving scenarios like overtaking, lane merging and jaywalking amongst many dynamic obstacles. Full thesis: pdf Centre for Robotics |
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