The Experts below are selected from a list of 107622 Experts worldwide ranked by ideXlab platform
Francesco Borrelli - One of the best experts on this subject based on the ideXlab platform.
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A learning-based framework for velocity control in autonomous driving
IEEE Transactions on Automation Science and Engineering, 2016Co-Authors: Stephanie Lefevre, Ashwin Carvalho, Francesco BorrelliAbstract:We present a framework for autonomous driving which can learn from human demonstrations, and we apply it to the longitudinal control of an autonomous car. Offline, we Model car-following strategies from a set of example driving sequences. Online, the Model is used to compute accelerations which replicate what a human Driver would do in the same situation. This reference acceleration is tracked by a predictive controller which enforces a set of comfort and safety constraints before applying the final acceleration. The controller is designed to be robust to the uncertainty in the predicted motion of the preceding vehicle. In addition, we estimate the confidence of the Driver Model predictions and use it in the cost function of the predictive controller. As a result, we can handle cases where the training data used to learn the Driver Model does not provide sufficient information about how a human Driver would handle the current driving situation. The approach is validated using a combination of simulations and experiments on our autonomous vehicle.
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Autonomous car following: A learning-based approach
IEEE Intelligent Vehicles Symposium Proceedings, 2015Co-Authors: Stephanie Lefevre, Ashwin Carvalho, Francesco BorrelliAbstract:We propose a learning-based method for the longitudinal control of an autonomous vehicle on the highway. We use a Driver Model to generate acceleration inputs which are used as a reference by a Model predictive controller. The Driver Model is trained using real driving data, so that it can reproduce the Driver's behavior. We show the system's ability to reproduce different driving styles from different Drivers. By solving a constrained optimization problem, the Model predictive controller ensures that the control inputs applied to the vehicle satisfy some safety criteria. This is demonstrated on a vehicle by artificially creating potentially dangerous situations with virtual obstacles.
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Lane Keeping Assistance with Learning-Based Driver Model and Model Predictive Control
2014Co-Authors: Stephanie Lefevre, Yiqi Gao, Dizan Vasquez, H. Eric Tseng, Ruzena Bajcsy, Francesco BorrelliAbstract:This paper proposes a novel active Lane Keeping Assistance Systems (LKAS) which relies on a learning-based Driver Model. The Driver Model detects unintentional lane departures earlier than existing LKAS, and as a result the correction needed to keep the vehicle in the lane is smaller. When the controller has control of the car, the Driver Model estimates what the Driver would do to keep the car in the lane, and the controller tries to reproduce that behavior as much as possible so that the controlled motion feels comfortable for the Driver. The Driver Model combines a Hidden Markov Model and Gaussian Mixture Regression. The controller is a Nonlinear Model Predictive Controller. The results obtained with real data show that our Driver Model can reliably predict lane departures. The controller is able to keep the car in the lane when there is a risk of lane departure, and does so less intrusively than existing LKAS. Topics / Active safety and Driver assistance systems, Driver Modeling
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stochastic predictive control for semi autonomous vehicles with an uncertain Driver Model
International Conference on Intelligent Transportation Systems, 2013Co-Authors: Andrew Gray, Yiqi Gao, Karl J Hedrick, Theresa Lin, Francesco BorrelliAbstract:In this paper a robust control framework is proposed for the lane-keeping and obstacle avoidance of semi-autonomous ground vehicles. A robust Model Predictive Control framework (MPC) is used in order to enforce safety constraints with minimal control intervention. A stochastic Driver Model is used in closed-loop with a vehicle Model to obtain a distribution over future vehicle trajectories. The uncertainty in the prediction is converted to probabilistic constraints. The robust MPC computes the smallest corrective steering action needed to satisfy the safety constraints, to a given probability. Simulations of a Driver approaching multiple obstacles, with uncertainty obtained from measured data, show the effect of the proposed framework.
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robust predictive control for semi autonomous vehicles with an uncertain Driver Model
IEEE Intelligent Vehicles Symposium, 2013Co-Authors: Andrew Gray, Yiqi Gao, Karl J Hedrick, Francesco BorrelliAbstract:A robust control design is proposed for the lane-keeping and obstacle avoidance of semiautonomous ground vehicles. A robust Model Predictive Controller (MPC) is used in order to enforce safety constraints with minimal control intervention. An uncertain Driver Model is used to obtain sets of predicted vehicle trajectories in closed-loop with the predicted Driver's behavior. The robust MPC computes the smallest corrective steering action needed to keep the Driver safe for all predicted trajectories in the set. Simulations of a Driver approaching multiple obstacles, with uncertainty obtained from measured data, show the effect of the proposed framework.
Martin Treiber - One of the best experts on this subject based on the ideXlab platform.
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the intelligent Driver Model with stochasticity new insights into traffic flow oscillations
Transportation Research Part B-methodological, 2017Co-Authors: Martin Treiber, Arne KestingAbstract:Traffic flow oscillations, including traffic waves, are a common yet incompletely understood feature of congested traffic. Possible mechanisms include traffic flow instabilities, indifference regions or finite human perception thresholds (action points), and external acceleration noise. However, the relative importance of these factors in a given situation remains unclear. We bring light into this question by adding external noise and action points to the Intelligent Driver Model and other car-following Models thereby obtaining a minimal Model containing all three oscillation mechanisms. We show analytically that even in the subcritical regime of linearly stable flow (order parameter ϵ < 0), external white noise leads to spatiotemporal speed correlations “anticipating” the waves of the linearly unstable regime. Sufficiently far away from the threshold, the amplitude scales with (−ϵ)−0.5. By means of simulations and comparisons with experimental car platoons and bicycle traffic, we show that external noise and indifference regions with action points have essentially equivalent effects. Furthermore, flow instabilities dominate the oscillations on freeways while external noise or action points prevail at low desired speeds such as vehicular city or bicycle traffic. For bicycle traffic, noise can lead to fully developed waves even for single-file traffic in the subcritical regime.
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improved 2d intelligent Driver Model in the framework of three phase traffic theory simulating synchronized flow and concave growth pattern of traffic oscillations
Transportation Research Part F-traffic Psychology and Behaviour, 2016Co-Authors: Junfang Tian, Rui Jiang, Geng Li, Martin TreiberAbstract:This paper firstly show that 2 Dimensional Intelligent Driver Model (Jiang et al., 2014) is not able to replicate the synchronized traffic flow. Then we propose an improved Model by considering the difference between the driving behaviors at high speeds and that at low speeds, which is in the framework of three-phase traffic theory. Simulations show that the improved Model can reproduce the phase transition from synchronized flow to wide moving jams, the spatiotemporal patterns of traffic flow induced by traffic bottleneck, and the concave growth pattern of traffic oscillations (i.e. the standard deviation of the velocities of vehicles increases in a concave/linear way along the platoon). Validating results show that the empirical time series of traffic speed obtained from Floating Car Data can be well simulated as well.
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enhanced intelligent Driver Model to access the impact of driving strategies on traffic capacity
Philosophical Transactions of the Royal Society A, 2010Co-Authors: Arne Kesting, Martin Treiber, Dirk HelbingAbstract:With an increasing number of vehicles equipped with adaptive cruise control (ACC), the impact of such vehicles on the collective dynamics of traffic flow becomes relevant. By means of simulation, we investigate the influence of variable percentages of ACC vehicles on traffic flow characteristics. For simulating the ACC vehicles, we propose a new car-following Model that also serves as the basis of an ACC implementation in real cars. The Model is based on the intelligent Driver Model (IDM) and inherits its intuitive behavioural parameters: desired velocity, acceleration, comfortable deceleration and desired minimum time headway. It eliminates, however, the sometimes unrealistic behaviour of the IDM in cut-in situations with ensuing small gaps that regularly are caused by lane changes of other vehicles in dense or congested traffic. We simulate the influence of different ACC strategies on the maximum capacity before breakdown and the (dynamic) bottleneck capacity after breakdown. With a suitable strategy, we find sensitivities of the order of 0.3, i.e. 1 per cent more ACC vehicles will lead to an increase in the capacities by about 0.3 per cent. This sensitivity multiplies when considering travel times at actual breakdowns.
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enhanced intelligent Driver Model to access the impact of driving strategies on traffic capacity
arXiv: Physics and Society, 2009Co-Authors: Arne Kesting, Martin Treiber, Dirk HelbingAbstract:With an increasing number of vehicles equipped with adaptive cruise control (ACC), the impact of such vehicles on the collective dynamics of traffic flow becomes relevant. By means of simulation, we investigate the influence of variable percentages of ACC vehicles on traffic flow characteristics. For simulating the ACC vehicles, we propose a new car-following Model that also serves as basis of an ACC implementation in real cars. The Model is based on the Intelligent Driver Model [Treiber et al., Physical Review E 62, 1805 (2000)] and inherits its intuitive behavioural parameters: desired velocity, acceleration, comfortable deceleration, and desired minimum time headway. It eliminates, however, the sometimes unrealistic behaviour of the Intelligent Driver Model in cut-in situations with ensuing small gaps that regularly are caused by lane changes of other vehicles in dense or congested traffic. We simulate the influence of different ACC strategies on the maximum capacity before breakdown, and the (dynamic) bottleneck capacity after breakdown. With a suitable strategy, we find sensitivities of the order of 0.3, i.e., 1% more ACC vehicles will lead to an increase of the capacities by about 0.3%. This sensitivity multiplies when considering travel times at actual breakdowns.
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calibrating car following Models using trajectory data methodological study
arXiv: Physics and Society, 2008Co-Authors: Arne Kesting, Martin TreiberAbstract:The car-following behavior of individual Drivers in real city traffic is studied on the basis of (publicly available) trajectory datasets recorded by a vehicle equipped with an radar sensor. By means of a nonlinear optimization procedure based on a genetic algorithm, we calibrate the Intelligent Driver Model and the Velocity Difference Model by minimizing the deviations between the observed driving dynamics and the simulated trajectory when following the same leading vehicle. The reliability and robustness of the nonlinear fits are assessed by applying different optimization criteria, i.e., different measures for the deviations between two trajectories. The obtained errors are in the range between~11% and~29% which is consistent with typical error ranges obtained in previous studies. In addition, we found that the calibrated parameter values of the Velocity Difference Model strongly depend on the optimization criterion, while the Intelligent Driver Model is more robust in this respect. By applying an explicit delay to the Model input, we investigated the influence of a reaction time. Remarkably, we found a negligible influence of the reaction time indicating that Drivers compensate for their reaction time by anticipation. Furthermore, the parameter sets calibrated to a certain trajectory are applied to the other trajectories allowing for Model validation. The results indicate that ``intra-Driver variability'' rather than ``inter-Driver variability'' accounts for a large part of the calibration errors. The results are used to suggest some criteria towards a benchmarking of car-following Models.
Arne Kesting - One of the best experts on this subject based on the ideXlab platform.
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the intelligent Driver Model with stochasticity new insights into traffic flow oscillations
Transportation Research Part B-methodological, 2017Co-Authors: Martin Treiber, Arne KestingAbstract:Traffic flow oscillations, including traffic waves, are a common yet incompletely understood feature of congested traffic. Possible mechanisms include traffic flow instabilities, indifference regions or finite human perception thresholds (action points), and external acceleration noise. However, the relative importance of these factors in a given situation remains unclear. We bring light into this question by adding external noise and action points to the Intelligent Driver Model and other car-following Models thereby obtaining a minimal Model containing all three oscillation mechanisms. We show analytically that even in the subcritical regime of linearly stable flow (order parameter ϵ < 0), external white noise leads to spatiotemporal speed correlations “anticipating” the waves of the linearly unstable regime. Sufficiently far away from the threshold, the amplitude scales with (−ϵ)−0.5. By means of simulations and comparisons with experimental car platoons and bicycle traffic, we show that external noise and indifference regions with action points have essentially equivalent effects. Furthermore, flow instabilities dominate the oscillations on freeways while external noise or action points prevail at low desired speeds such as vehicular city or bicycle traffic. For bicycle traffic, noise can lead to fully developed waves even for single-file traffic in the subcritical regime.
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enhanced intelligent Driver Model to access the impact of driving strategies on traffic capacity
Philosophical Transactions of the Royal Society A, 2010Co-Authors: Arne Kesting, Martin Treiber, Dirk HelbingAbstract:With an increasing number of vehicles equipped with adaptive cruise control (ACC), the impact of such vehicles on the collective dynamics of traffic flow becomes relevant. By means of simulation, we investigate the influence of variable percentages of ACC vehicles on traffic flow characteristics. For simulating the ACC vehicles, we propose a new car-following Model that also serves as the basis of an ACC implementation in real cars. The Model is based on the intelligent Driver Model (IDM) and inherits its intuitive behavioural parameters: desired velocity, acceleration, comfortable deceleration and desired minimum time headway. It eliminates, however, the sometimes unrealistic behaviour of the IDM in cut-in situations with ensuing small gaps that regularly are caused by lane changes of other vehicles in dense or congested traffic. We simulate the influence of different ACC strategies on the maximum capacity before breakdown and the (dynamic) bottleneck capacity after breakdown. With a suitable strategy, we find sensitivities of the order of 0.3, i.e. 1 per cent more ACC vehicles will lead to an increase in the capacities by about 0.3 per cent. This sensitivity multiplies when considering travel times at actual breakdowns.
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enhanced intelligent Driver Model to access the impact of driving strategies on traffic capacity
arXiv: Physics and Society, 2009Co-Authors: Arne Kesting, Martin Treiber, Dirk HelbingAbstract:With an increasing number of vehicles equipped with adaptive cruise control (ACC), the impact of such vehicles on the collective dynamics of traffic flow becomes relevant. By means of simulation, we investigate the influence of variable percentages of ACC vehicles on traffic flow characteristics. For simulating the ACC vehicles, we propose a new car-following Model that also serves as basis of an ACC implementation in real cars. The Model is based on the Intelligent Driver Model [Treiber et al., Physical Review E 62, 1805 (2000)] and inherits its intuitive behavioural parameters: desired velocity, acceleration, comfortable deceleration, and desired minimum time headway. It eliminates, however, the sometimes unrealistic behaviour of the Intelligent Driver Model in cut-in situations with ensuing small gaps that regularly are caused by lane changes of other vehicles in dense or congested traffic. We simulate the influence of different ACC strategies on the maximum capacity before breakdown, and the (dynamic) bottleneck capacity after breakdown. With a suitable strategy, we find sensitivities of the order of 0.3, i.e., 1% more ACC vehicles will lead to an increase of the capacities by about 0.3%. This sensitivity multiplies when considering travel times at actual breakdowns.
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calibrating car following Models using trajectory data methodological study
arXiv: Physics and Society, 2008Co-Authors: Arne Kesting, Martin TreiberAbstract:The car-following behavior of individual Drivers in real city traffic is studied on the basis of (publicly available) trajectory datasets recorded by a vehicle equipped with an radar sensor. By means of a nonlinear optimization procedure based on a genetic algorithm, we calibrate the Intelligent Driver Model and the Velocity Difference Model by minimizing the deviations between the observed driving dynamics and the simulated trajectory when following the same leading vehicle. The reliability and robustness of the nonlinear fits are assessed by applying different optimization criteria, i.e., different measures for the deviations between two trajectories. The obtained errors are in the range between~11% and~29% which is consistent with typical error ranges obtained in previous studies. In addition, we found that the calibrated parameter values of the Velocity Difference Model strongly depend on the optimization criterion, while the Intelligent Driver Model is more robust in this respect. By applying an explicit delay to the Model input, we investigated the influence of a reaction time. Remarkably, we found a negligible influence of the reaction time indicating that Drivers compensate for their reaction time by anticipation. Furthermore, the parameter sets calibrated to a certain trajectory are applied to the other trajectories allowing for Model validation. The results indicate that ``intra-Driver variability'' rather than ``inter-Driver variability'' accounts for a large part of the calibration errors. The results are used to suggest some criteria towards a benchmarking of car-following Models.
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calibrating car following Models by using trajectory data methodological study
Transportation Research Record, 2008Co-Authors: Arne Kesting, Martin TreiberAbstract:The car-following behavior of individual Drivers in real city traffic is studied on the basis of (publicly available) trajectory data sets recorded by a vehicle equipped with a radar sensor. By means of a nonlinear optimization procedure based on a genetic algorithm, the intelligent Driver Model and the velocity difference Model are calibrated by minimizing the deviations between the observed driving dynamics and the simulated trajectory in following the same leading vehicle. The reliability and robustness of the nonlinear fits are assessed by applying different optimization criteria, that is, different measures for the deviations between two trajectories. The obtained errors are between 11% and 29%, which is consistent with typical error ranges obtained in previous studies. It is also found that the calibrated parameter values of the velocity difference Model depend strongly on the optimization criterion, whereas the intelligent Driver Model is more robust. The influence of a reaction time is investigate...
Alain Berthoz - One of the best experts on this subject based on the ideXlab platform.
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role of lateral acceleration in curve driving Driver Model and experiments on a real vehicle and a driving simulator
Human Factors, 2001Co-Authors: Gilles Reymond, Andras Kemeny, Jacques Droulez, Alain BerthozAbstract:Experimental studies show that automobile Drivers adjust their speed in curves so that maximum vehicle lateral accelerations decrease at high speeds. This pattern of lateral accelerations is described by a new Driver Model, assuming Drivers control a variable safety margin of perceived lateral acceleration according to their anticipated steering deviations. Compared with a minimum time-to-lane-crossing (H. Godthelp, 1986) speed modulation strategy, this Model, based on nonvisual cues, predicts that extreme values of lateral acceleration in curves decrease quadratically with speed, in accordance with experimental data obtained in a vehicle driven on a test track and in a motion-based driving simulator. Variations of Model parameters can characterize "normal" or "fast" driving styles on the test track. On the simulator, it was found that the upper limits of lateral acceleration decreased less steeply when the motion cuing system was deactivated, although Drivers maintained a consistent driving style. This i...
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Role of Lateral Acceleration in Curve Driving: Driver Model and Experiments on a Real Vehicle and a Driving Simulator
Human Factors: The Journal of the Human Factors and Ergonomics Society, 2001Co-Authors: Gilles Reymond, Andras Kemeny, Jacques Droulez, Alain BerthozAbstract:Experimental studies show that automobile Drivers adjust their speed in curves so that maximum vehicle lateral accelerations decrease at high speeds. This pattern of lateral accelerations is described by a new Driver Model, assuming Drivers control a variable safety margin of perceived lateral acceleration according to their anticipated steering deviations. Compared with a minimum time-to-lane-crossing (H. Godthelp, 1986) speed modulation strategy, this Model, based on nonvisual cues, predicts that extreme values of lateral acceleration in curves decrease quadratically with speed, in accordance with experimental data obtained in a vehicle driven on a test track and in a motion-based driving simulator. Variations of Model parameters can characterize "normal" or "fast" driving styles on the test track. On the simulator, it was found that the upper limits of lateral acceleration decreased less steeply when the motion cuing system was deactivated, although Drivers maintained a consistent driving style. This is interpreted per the Model as an underestimation of curvilinear speed due to the lack of inertial stimuli. Actual or potential applications of this research include a method to assess driving simulators as well as to identify driving styles for on-board Driver aid systems.
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Role of Lateral Acceleration in Curve Driving: Driver Model and Experiments on a Real Vehicle and a Driving Simulator
Human Factors: The Journal of the Human Factors and Ergonomics Society, 2001Co-Authors: Gilles Reymond, Andras Kemeny, Jacques Droulez, Alain BerthozAbstract:Experimental studies show that automobile Drivers adjust their speed in curves so that maximum vehicle lateral accelerations decrease at high speeds. This pattern of lateral accelerations is described by a new Driver Model, assuming Drivers control a variable safety margin of perceived lateral acceleration according to their anticipated steering deviations. Compared with a minimum time-to-lane-crossing (H. Godthelp, 1986) speed modulation strategy, this Model, based on nonvisual cues, predicts that extreme values of lateral acceleration in curves decrease quadratically with speed, in accordance with experimental data obtained in a vehicle driven on a test track and in a motion-based driving simulator. Variations of Model parameters can characterize "normal" or "fast" driving styles on the test track. On the simulator, it was found that the upper limits of lateral acceleration decreased less steeply when the motion cuing system was deactivated, although Drivers maintained a consistent driving style. This is interpreted per the Model as an underestimation of curvilinear speed due to the lack of inertial stimuli. Actual or potential applications of this research include a method to assess driving simulators as well as to identify driving styles for on-board Driver aid systems.
B. Rajkumar - One of the best experts on this subject based on the ideXlab platform.
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fractional order fuzzy sliding mode controller for the quarter car with Driver Model and dual actuators
IET electrical systems in transportation, 2017Co-Authors: Subramanian Rajendiran, P Lakshmi, B. RajkumarAbstract:The ride quality and travel comfort of the passenger is based on the type of the suspension system used in the vehicle. The active suspension system is one of the good choices to reduce the vibration and enhance the travel comfort. In this study, a quarter car with integrated seat suspension and Driver Model (QCSD) is considered for analysis. The controllers are designed for both single actuator (SA) and dual actuator (DA). To reduce the vibration and increase the travel comfort, different types of sliding mode controllers (SMCs) such as fuzzy SMC (FSMC), fractional order SMC and fractional order FSMC (FrFSMC) are designed and simulated in the active suspension system of the QCSD. Three types of road disturbances are used to stimulate the vibration in the system. The responses of the controllers with the QCSD are compared with the passive system and existing state feedback controller. The result shows that the FrFSMC performs better than the other controllers for DA as well as SA. While comparing the DA and SA, DA performs better than SA.
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Vibration control of Quarter car integrated seat suspension with Driver Model for different road profiles using fuzzy based sliding mode controller
2015 Seventh International Conference on Advanced Computing (ICoAC), 2015Co-Authors: B. Rajkumar, P Lakshmi, Subramani RajendiranAbstract:Vibration control of a Quarter car Model is one of the important factor to improve the travel comfort of the passenger. Many researchers developed control strategies for 2 Degree Of Freedom (DOF) quarter car Model. In this paper, travel comfort of the passenger is analyzed by designing and simulating the Fuzzy Logic Controller (FLC), Sliding Mode Controller (SMC) and Fuzzy SMC (FSMC) for an 8 DOF quarter car with integrated Seat suspension and Driver Model. While testing the performance of the controllers, the system is subjected to four types of road disturbance individually. The responses are compared with each other along with the passive system. The results show that FSMC reduced the Vibration (89.97%) than the FLC, SMC and passive system.
