With the objective to improve road safety, the automotive industry is moving toward more “intelligent” vehicles. One of the major challenges is to detect dangerous situations and react accordingly in order to avoid or mitigate accidents. This requires predicting the likely evolution of the current traffic situation, and assessing how dangerous that future situation might be. This paper is a survey of existing methods for motion prediction and risk assessment for intelligent vehicles. The proposed classification is based on the semantics used to define motion and risk. We point out the tradeoff between model completeness and real-time constraints, and the fact that the choice of a risk assessment method is influenced by the selected motion model.

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A survey on motion prediction and risk assessment for intelligent vehicles

A path planning and tracking framework is presented to maintain a collision-free path for autonomous vehicles. For path-planning approaches, a 3-D virtual dangerous potential field is constructed as a superposition of trigonometric functions of the road and the exponential function of obstacles, which can generate a desired trajectory for collision avoidance when a vehicle collision with obstacles is likely to happen. Next, to track the planned trajectory for collision avoidance maneuvers, the path-tracking controller formulated the tracking task as a multiconstrained model predictive control (MMPC) problem and calculated the front steering angle to prevent the vehicle from colliding with a moving obstacle vehicle. Simulink and CarSim simulations are conducted in the case where moving obstacles exist. The simulation results show that the proposed path-planning approach is effective for many driving scenarios, and the MMPC-based path-tracking controller provides dynamic tracking performance and maintains good maneuverability.

Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints

Model predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems [1], [2]. The conceptual simplicity of MPC as well as its ability to effectively cope with the complex dynamics of systems with multiple inputs and outputs, input and state/output constraints, and conflicting control objectives have made it an attractive multivariable constrained control approach [1]. MPC (a.k.a. receding-horizon control) solves an open-loop constrained optimal control problem (OCP) repeatedly in a receding-horizon manner [3]. The OCP is solved over a finite sequence of control actions {u0,u1,f,uN- 1} at every sampling time instant that the current state of the system is measured. The first element of the sequence of optimal control actions is applied to the system, and the computations are then repeated at the next sampling time. Thus, MPC replaces a feedback control law p(m), which can have formidable offline computation, with the repeated solution of an open-loop OCP [2]. In fact, repeated solution of the OCP confers an "implicit" feedback action to MPC to cope with system uncertainties and disturbances. Alternatively, explicit MPC approaches circumvent the need to solve an OCP online by deriving relationships for the optimal control actions in terms of an "explicit" function of the state and reference vectors. However, explicit MPC is not typically intended to replace standard MPC but, rather, to extend its area of application [4]-[6].

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Stochastic Model Predictive Control: An Overview and Perspectives for Future Research

We study the use of kinematic and dynamic vehicle models for model-based control design used in autonomous driving In particular, we analyze the statistics of the forecast error of these two models by using experimental data In addition, we study the effect of discretization on forecast error We use the results of the first part to motivate the design of a controller for an autonomous vehicle using model predictive control (MPC) and a simple kinematic bicycle model The proposed approach is less computationally expensive than existing methods which use vehicle tire models Moreover it can be implemented at low vehicle speeds where tire models become singular Experimental results show the effectiveness of the proposed approach at various speeds on windy roads

Kinematic and dynamic vehicle models for autonomous driving control design

In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning,...

Planning and Decision-Making for Autonomous Vehicles

Two frameworks based on Model Predictive Control (MPC) for obstacle avoidance with autonomous vehicles are presented. A given trajectory represents the driver intent. An MPC has to safely avoid obstacles on the road while trying to track the desired trajectory by controlling front steering angle and differential braking. We present two different approaches to this problem. The first approach solves a single nonlinear MPC problem. The second approach uses a hierarchical scheme. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model in order to avoid an obstacle. At the low-level an MPC controller computes the vehicle inputs in order to best follow the high level trajectory based on a nonlinear vehicle model. This article presents the design and comparison of both approaches, the method for implementing them, and successful experimental results on icy roads.Copyright © 2010 by ASME

Predictive Control of Autonomous Ground Vehicles With Obstacle Avoidance on Slippery Roads

This paper presents a hierarchical control framework for the obstacle avoidance of autonomous and semi-autonomous ground vehicles. The high-level planner is based on motion primitives created from a four-wheel nonlinear dynamic model. Parameterized clothoids and drifting maneuvers are used to improve vehicle agility. The low-level tracks the planned trajectory with a nonlinear Model Predictive Controller. The first part of the paper describes the proposed control architecture and methodology. The second part presents simulative and experimental results with an autonomous and semi-autonomous ground vehicle traveling at high speed on an icy surface.

Predictive control for agile semi-autonomous ground vehicles using motion primitives

This paper proposes a robust control framework for lane-keeping and obstacle avoidance of semiautonomous ground vehicles. It presents a systematic way of enforcing robustness during the MPC design stage. A robust nonlinear model predictive controller (RNMPC) is used to help the driver navigating the vehicle in order to avoid obstacles and track the road centre line. A force-input nonlinear bicycle vehicle model is developed and used in the RNMPC control design. A robust invariant set is used in the RNMPC design to guarantee that state and input constraints are satisfied in the presence of disturbances and model error. Simulations and experiments on a vehicle show the effectiveness of the proposed framework.

A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles

This paper presents the design of a controller for an autonomous ground vehicle. The goal is to track the lane centerline while avoiding collisions with obstacles. A nonlinear model predictive control (MPC) framework is used where the control inputs are the front steering angle and the braking torques at the four wheels. The focus of this work is on the development of a tailored algorithm for solving the nonlinear MPC problem. Hardware-in-the-loop simulations with the proposed algorithm show a reduction in the computational time as compared to general purpose nonlinear solvers. Experimental tests on a passenger vehicle at high speeds on low friction road surfaces show the effectiveness of the proposed algorithm.

Predictive control of an autonomous ground vehicle using an iterative linearization approach

Threat assessment during semiautonomous driving is used to determine when correcting a driver's input is required. Since current semiautonomous systems perform threat assessment by predicting a vehicle's future state while treating the driver's input as a disturbance, autonomous controller intervention is limited to a restricted regime. Improving vehicle safety demands threat assessment that occurs over longer prediction horizons wherein a driver cannot be treated as a malicious agent. In this paper, we describe a real-time semiautonomous system that utilizes empirical observations of a driver's pose to inform an autonomous controller that corrects a driver's input when possible in a safe manner. We measure the performance of our system using several metrics that evaluate the informativeness of the prediction and the utility of the intervention procedure. A multisubject driving experiment illustrates the usefulness, with respect to these metrics, of incorporating the driver's pose while designing a semiautonomous system.

Yiqi Gao

Papers

Predictive Control of Autonomous Ground Vehicles With Obstacle Avoidance on Slippery Roads

Predictive control for agile semi-autonomous ground vehicles using motion primitives

A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles

Predictive control of an autonomous ground vehicle using an iterative linearization approach

Semiautonomous Vehicular Control Using Driver Modeling