2,313 research outputs found
Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios
Uncertainty in the behavior of other traffic participants is a crucial factor
in collision avoidance for automated driving; here, stochastic metrics should
often be considered to avoid overly conservative decisions. This paper
introduces a Stochastic Model Predictive Control (SMPC) planner for emergency
collision avoidance in highway scenarios to proactively minimize collision risk
while ensuring safety through chance constraints. To address the challenge of
guaranteeing the feasibility for the emergency trajectory, we incorporate
nonlinear tire dynamics in the prediction model of the ego vehicle. Further, we
exploit Max-Min-Plus-Scaling (MMPS) approximations of the nonlinearities to
avoid conservatism, enforce proactive collision avoidance, and improve
computational efficiency in terms of performance and speed. Consequently, our
contributions include integrating a dynamic ego vehicle model into the SMPC
planner, introducing the MMPS approximation for real-time implementation in
emergency scenarios, and integrating SMPC with hybridized chance constraints
and risk minimization. We evaluate our SMPC formulation in terms of proactivity
and efficiency in various hazardous scenarios. Moreover, we demonstrate the
effectiveness of our proposed approach by comparing it with a state-of-the-art
SMPC planner and validate the feasibility of generated trajectories using a
high-fidelity vehicle model in IPG CarMaker.Comment: 13 pages, 10 figures, submitted to IEEE Transactions on Control
Systems Technolog
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
Predictive Control for Autonomous Driving with Uncertain, Multi-modal Predictions
We propose a Stochastic MPC (SMPC) formulation for path planning with
autonomous vehicles in scenarios involving multiple agents with multi-modal
predictions. The multi-modal predictions capture the uncertainty of urban
driving in distinct modes/maneuvers (e.g., yield, keep speed) and driving
trajectories (e.g., speed, turning radius), which are incorporated for
multi-modal collision avoidance chance constraints for path planning. In the
presence of multi-modal uncertainties, it is challenging to reliably compute
feasible path planning solutions at real-time frequencies ( 10 Hz). Our
main technological contribution is a convex SMPC formulation that
simultaneously (1) optimizes over parameterized feedback policies and (2)
allocates risk levels for each mode of the prediction. The use of feedback
policies and risk allocation enhances the feasibility and performance of the
SMPC formulation against multi-modal predictions with large uncertainty. We
evaluate our approach via simulations and road experiments with a full-scale
vehicle interacting in closed-loop with virtual vehicles. We consider distinct,
multi-modal driving scenarios: 1) Negotiating a traffic light and a fast,
tailgating agent, 2) Executing an unprotected left turn at a traffic
intersection, and 3) Changing lanes in the presence of multiple agents. For all
of these scenarios, our approach reliably computes multi-modal solutions to the
path-planning problem at real-time frequencies.Comment: The first three authors contributed equall
Data-Driven Robust Optimization for Energy-Aware and Safe Navigation of Electric Vehicles
In this paper, we simultaneously tackle the problem of energy optimal and
safe navigation of electric vehicles in a data-driven robust optimization
framework. We consider a dynamic model of the electric vehicle which includes
kinematic variables in both inertial and body coordinate systems in order to
capture both longitudinal and lateral motion as well as state-of-energy of the
battery. We leverage past data of obstacle motion to construct a future
occupancy set with probabilistic guarantees, and formulate robust collision
avoidance constraints with respect to such an occupancy set using convex
programming duality. Consequently, we present the finite horizon optimal
control problem subject to robust collision avoidance constraints while
penalizing resulting energy consumption. Finally, we show the effectiveness of
the proposed approach in reducing energy consumption and ensuring safe
navigation via extensive simulations involving curved roads and multiple
obstacles
A Human Driver Model for Autonomous Lane Changing in Highways: Predictive Fuzzy Markov Game Driving Strategy
This study presents an integrated hybrid solution to mandatory lane changing problem
to deal with accident avoidance by choosing a safe gap in highway driving. To manage
this, a comprehensive treatment to a lane change active safety design is proposed from
dynamics, control, and decision making aspects.
My effort first goes on driver behaviors and relating human reasoning of threat in
driving for modeling a decision making strategy. It consists of two main parts; threat assessment
in traffic participants, (TV s) states, and decision making. The first part utilizes
an complementary threat assessment of TV s, relative to the subject vehicle, SV , by evaluating
the traffic quantities. Then I propose a decision strategy, which is based on Markov
decision processes (MDPs) that abstract the traffic environment with a set of actions, transition
probabilities, and corresponding utility rewards. Further, the interactions of the TV s
are employed to set up a real traffic condition by using game theoretic approach. The question
to be addressed here is that how an autonomous vehicle optimally interacts with the
surrounding vehicles for a gap selection so that more effective performance of the overall
traffic flow can be captured. Finding a safe gap is performed via maximizing an objective
function among several candidates. A future prediction engine thus is embedded in the
design, which simulates and seeks for a solution such that the objective function is maximized
at each time step over a horizon. The combined system therefore forms a predictive
fuzzy Markov game (FMG) since it is to perform a predictive interactive driving strategy
to avoid accidents for a given traffic environment. I show the effect of interactions in decision
making process by proposing both cooperative and non-cooperative Markov game
strategies for enhanced traffic safety and mobility. This level is called the higher level
controller. I further focus on generating a driver controller to complement the automated
car’s safe driving. To compute this, model predictive controller (MPC) is utilized. The
success of the combined decision process and trajectory generation is evaluated with a set
of different traffic scenarios in dSPACE virtual driving environment.
Next, I consider designing an active front steering (AFS) and direct yaw moment control
(DYC) as the lower level controller that performs a lane change task with enhanced
handling performance in the presence of varying front and rear cornering stiffnesses. I propose
a new control scheme that integrates active front steering and the direct yaw moment
control to enhance the vehicle handling and stability. I obtain the nonlinear tire forces
with Pacejka model, and convert the nonlinear tire stiffnesses to parameter space to design
a linear parameter varying controller (LPV) for combined AFS and DYC to perform a
commanded lane change task. Further, the nonlinear vehicle lateral dynamics is modeled
with Takagi-Sugeno (T-S) framework. A state-feedback fuzzy H∞ controller is designed
for both stability and tracking reference. Simulation study confirms that the performance
of the proposed methods is quite satisfactory
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