Jongsang Suh

Bio: Jongsang Suh is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Model predictive control & Motion planning. The author has an hindex of 5, co-authored 7 publications receiving 127 citations. Previous affiliations of Jongsang Suh include Seoul National University.

Stochastic Model-Predictive Control for Lane Change Decision of Automated Driving Vehicles

Abstract: This paper describes lane change motion planning with a combination of probabilistic and deterministic prediction for automated driving under complex driving circumstances. The autonomous lane change should arrive safely at the destination. The subject vehicle needs to perceive and predict the behaviors of other vehicles with sensors. From the information of other vehicles, a collision probability is defined using a reachable set of uncertainty propagation. In addition, the lane change risk is monitored using predicted time-to-collision and safety distance to guarantee safety in lane change behavior. A safe driving envelope is defined as constraints based on the combinatorial prediction (probabilistic and deterministic) of the behavior of surrounding vehicles. To obtain the desired steering angle and longitudinal acceleration to maintain the automated driving vehicle under constraints, a stochastic model-predictive control problem is formulated. The proposed model has been evaluated by performing lane change simulations in MATLAB/Simulink, while considering the effect of combination prediction. Also, the proposed algorithm has been implemented on a test vehicle. The simulation and test results show that the proposed algorithm can handle complicated lane change scenarios, while guaranteeing safety.

Stochastic predictive control based motion planning for lane change decision using a Vehicle Traffic Simulator

Abstract: This paper describes the design and evaluation of a model predictive control algorithm for automated driving on a motorway using a Vehicle Traffic Simulator. For the development of a highly automated driving control algorithm, motion planning is necessary to satisfy driving condition in various road traffic situations. There are two key issues in motion planning of automated driving vehicles. One of the key issues is how to handle potentially dangerous situations that could occur in order to guarantee the safety of vehicles. The second key issue is how to guarantee the robustness of the controller under model uncertainties and external disturbances. To improve safety with respect to the future behaviors of subject vehicles, not the current states but rather the predicted behaviors of surrounding vehicles should be considered. The desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope during a finite prediction horizon, a motion planning controller is designed based on an MPC approach. The developed control algorithm has been successfully implemented on a vehicle ECU. The proposed control algorithm has been evaluated on a real-time vehicle traffic simulator. The throttle, brake, and steering control inputs and the controlled vehicle behavior have been compared to those of manual driving.

A novel method for identifying inertial parameters of electric vehicles based on the dual H infinity filter

Abstract: Accurate identification of vehicle inertial parameters is essential to the design of vehicle dynamics control systems. In this paper, a novel vehicle inertial parameter identification metho...

Fault Diagnosis of an Autonomous Vehicle With an Improved SVM Algorithm Subject to Unbalanced Datasets

Abstract: Safety is one of the key requirements for automated vehicles and fault diagnosis is an effective technique to enhance the vehicle safety. The model-based fault diagnosis method models the fault into the system model and estimates the faults by observer. In this article, to avoid the complexity of designing observer, we investigate the problem of steering actuator fault diagnosis for automated vehicles based on the approach of model-based support vector machine (SVM) classification. The system model is utilized to generate the residual signal as the training data and the data-based algorithm of the SVM classification is employed to diagnose the fault. Due to the phenomena of data unbalance induced poor performance of the data-driven method, an undersampling procedure with the approach of linear discriminant analysis and a threshold adjustment using the algorithm of grey wolf optimizer are proposed to modify and improve the performance of classification and fault diagnosis. Various comparisons are carried out based on widely used datasets. The comparison results show that the proposed algorithm has superiority on the classification over existing methods. Experimental results and comparisons of an automated vehicle illustrate the effectiveness of the proposed algorithm on the steering actuator fault diagnosis.

Stochastic Model-Predictive Control for Lane Change Decision of Automated Driving Vehicles

Abstract: This paper describes lane change motion planning with a combination of probabilistic and deterministic prediction for automated driving under complex driving circumstances. The autonomous lane change should arrive safely at the destination. The subject vehicle needs to perceive and predict the behaviors of other vehicles with sensors. From the information of other vehicles, a collision probability is defined using a reachable set of uncertainty propagation. In addition, the lane change risk is monitored using predicted time-to-collision and safety distance to guarantee safety in lane change behavior. A safe driving envelope is defined as constraints based on the combinatorial prediction (probabilistic and deterministic) of the behavior of surrounding vehicles. To obtain the desired steering angle and longitudinal acceleration to maintain the automated driving vehicle under constraints, a stochastic model-predictive control problem is formulated. The proposed model has been evaluated by performing lane change simulations in MATLAB/Simulink, while considering the effect of combination prediction. Also, the proposed algorithm has been implemented on a test vehicle. The simulation and test results show that the proposed algorithm can handle complicated lane change scenarios, while guaranteeing safety.

ML-Based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection

Abstract: The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents. While AV development today involves verification, validation, and testing, end-to-end assessment of AV systems under accidental faults in realistic driving scenarios has been largely unexplored. This paper presents DriveFI, a machine learning-based fault injection engine, which can mine situations and faults that maximally impact AV safety, as demonstrated on two industry-grade AV technology stacks (from NVIDIA and Baidu). For example, DriveFI found 561 safety-critical faults in less than 4 hours. In comparison, random injection experiments executed over several weeks could not find any safety-critical faults.

AV-FUZZER: Finding Safety Violations in Autonomous Driving Systems

Abstract: This paper proposes AV-FUZZER, a testing framework, to find the safety violations of an autonomous vehicle (AV) in the presence of an evolving traffic environment. We perturb the driving maneuvers of traffic participants to create situations in which an AV can run into safety violations. To optimally search for the perturbations to be introduced, we leverage domain knowledge of vehicle dynamics and genetic algorithm to minimize the safety potential of an AV over its projected trajectory. The values of the perturbation determined by this process provide parameters that define participants’ trajectories. To improve the efficiency of the search, we design a local fuzzer that increases the exploitation of local optima in the areas where highly likely safety-hazardous situations are observed. By repeating the optimization with significantly different starting points in the search space, AV-FUZZER determines several diverse AV safety violations. We demonstrate AV-FUZZER on an industrial-grade AV platform, Baidu Apollo, and find five distinct types of safety violations in a short period of time. In comparison, other existing techniques can find at most two. We analyze the safety violations found in Apollo and discuss their overarching causes.

Fuzzy Static Output Feedback Control for Path Following of Autonomous Vehicles With Transient Performance Improvements

Abstract: This paper provides a new solution for path following control of autonomous ground vehicles. $\mathcal {H}_{2}$ control problem is considered to attenuate the effect of the road curvature disturbance. To this end, we formulate a standard model from the road-vehicle dynamics, the a priori knowledge on the road curvature, and the path following specifications. This standard model is then represented in a Takagi–Sugeno fuzzy form to deal with the time-varying nature of the vehicle speed. Based on a static output feedback scheme, the proposed method allows avoiding expensive vehicle sensors while keeping the simplest control structure for real-time implementation. The concept of $\mathcal {D}-$ stability is exploited using Lyapunov stability arguments to improve the transient behaviors of the closed-loop vehicle system. In particular, the physical upper and lower bounds of the vehicle acceleration are explicitly considered in the design procedure via a parameter-dependent Lyapunov function to reduce drastically the design conservatism. The proposed $\mathcal {H}_{2}$ design conditions are expressed in terms of linear matrix inequalities (LMIs) with a single line search parameter. The effectiveness of the new path following control method is clearly demonstrated with both theoretical illustrations and hardware experiments under real-world driving situations.