Predictive Active Steering Control for Autonomous Vehicle Systems

By Paolo Falcone, Francesco Borrelli, Jahan Asgari, H. E. Tseng, Davor Hrovat. 2007

1,337 citations recorded by the source corpus.

In this paper, a model predictive control (MPC) approach for controlling an active front steering system in an autonomous vehicle is presented. At each time step, a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We present two approaches with different computational complexities. In the first approach, we formulate the MPC problem by using a nonlinear vehicle model. The second approach is based on successive online linearization of the vehicle model. Discussions on computational complexity and performance of the two schemes are presented. The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads

Subfields: Automotive Engineering, Control and Systems Engineering

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Citations in the corpus

A survey of data fusion in smart city applications (2019, methodology): “As for the autonomous vehicle system, the control of an autonomous vehicle itself is a complex data fusion process, fusing various data sources about the vehicle and the road by advanced machine learning and control algorithms [140], [206].”
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles (2016, methodology): “3) Trajectory Tracking Controllers: A predictive controller using a dynamic model with nonlinear tire behavior was investigated in [138].”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, background): “An approach with nonlinear tire behavior for tracking trajectory on various road conditions is explored in [267], and the simulation results suggest that the vehicle can be stabilized on challenging icy surfaces at a 20 Hz control frequency.”
Planning and Decision-Making for Autonomous Vehicles (2018, background): “Nonlinear control (18), model predictive control (19), or feedback–feedforward control (20) stabilizes the behavior of the vehicle while tracking the specified path.”
Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021, background): “Artiﬁcial Intelligence (AI), generally, and Machine Learning (ML), speciﬁcally, have demonstrated remarkable potential in varied application domains, from self-driving cars [1] to beating humans at increasingly complex games such as Go [2].”
Collision Avoidance and Stabilization for Autonomous Vehicles in Emergency Scenarios (2017, methodology): “5) often used [6], [11], [12], to all slip angles that can generate additional force.”
Variational End-to-End Navigation and Localization (2018, methodology): “As opposed to traditional methods for autonomous driving which typically rely on distinct algorithms for localization and mapping [7], [8], [9], planning [10], [11], [12], and control [13], [14], end-to-end algorithms attempt to collapse the problem (directly from raw sensory data to output control ”
Variational Autoencoder for End-to-End Control of Autonomous Driving with Novelty Detection and Training De-biasing (2018, background): “These submodules can range from mapping and localization [3], [4], to perception [5]–[7], planning [8], [9], and control [10], [11].”
A unified approach to local trajectory planning and control for autonomous driving along a reference path (2014, background): “As for the deterministic motion planner, a recent wellknown research on this field is the model predictive control (MPC) [5].”
A Survey of End-to-End Driving: Architectures and Training Methods (2020, methodology): “The cost maps are then used to pick a good trajectory and model predictive control (MPC) [109] computes the necessary lowlevel commands.”
Maneuver-Based Trajectory Planning for Highly Autonomous Vehicles on Real Road With Traffic and Driver Interaction (2010, methodology): “Some of the works use this technique to solve a part of the problem, such as [42] (for the longitudinal movement, without the presence of objects), [43] (for the vehicle control part), or [44] (for the geometry of an obstacle avoidance trajectory), without integrating the complete decisional part.”