Wenshuo Wang

Wenshuo Wang
University of California, Berkeley | UCB

Post-Doc

About

70
Publications
29,219
Reads
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1,412
Citations
Additional affiliations
October 2019 - present
University of California, Berkeley
Position
  • PostDoc Position
July 2018 - October 2019
Carnegie Mellon University
Position
  • PostDoc Position
September 2015 - present
University of California, Berkeley
Position
  • PhD Student
Education
September 2017 - July 2018
September 2015 - September 2017
University of California, Berkeley
Field of study
  • Vehicle Dynamics Control, Driver model, Pattern Recognition, Machine Learning
September 2012 - August 2015
Beijing Institute of Technology
Field of study
  • Vehicle Dynamics control, driver model

Publications

Publications (70)
Article
Driver model adaptation (DMA) plays an essential role for driving behaviour modelling when there is a lack of sufficient data for training the new model. A new data-driven DMA method is proposed in this paper to realise the instance-level knowledge transfer between individual drivers. Using the importance-weighted transfer learning (IWTL), the data...
Article
Humans make daily routine decisions based on their internal states in intricate interaction scenarios. This article presents a probabilistically reconstructive learning approach to identify the internal states of multivehicle sequential interactions when merging at highway on-ramps. We treated the merging task's sequential decision as a dynamic, st...
Article
Merging at highway on-ramps while interacting with other human-driven vehicles is challenging for autonomous vehicles (AVs). An efficient route to this challenge requires exploring and exploiting knowledge of the interaction process from demonstrations by humans. However, it is unclear what information (or environmental states) is utilized by the h...
Article
Accurate speed prediction is practically critical to eco-safe driving for intelligent vehicles. Existing research only makes vehicles adapt to the dynamic driving environment while rarely considering the influence of human driving preferences. This paper proposes a learning-based model to leverage human driving preferences into speed prediction. We...
Article
Full-text available
Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowledge of specific scenarios with predefined models, limiting their adaptive capabilities. This paper describes a Bayesian nonparametric approach that leverages continuous (i...
Preprint
Full-text available
Humans make daily-routine decisions based on their internal states in intricate interaction scenarios. This paper presents a probabilistically reconstructive learning approach to identify the internal states of multi-vehicle sequential interactions when merging at highway on-ramps. We treated the merging task's sequential decision as a dynamic, sto...
Conference Paper
Full-text available
Reliable representation of multi-vehicle interactions in urban traffic is pivotal but challenging for autonomous vehicles due to the volatility of the traffic environment, such as roundabouts and intersections. This paper describes a semi-stochastic potential field approach to represent multi-vehicle interactions by integrating a deterministic fiel...
Article
Understanding driver interaction behavioral semantics has potential benefits to autonomous car's decision-making design. This article presents a framework of analyzing various encountering behaviors through decomposing driving encounter sequential data into small building blocks, called traffic primitives, using a Bayesian nonparametric learning (B...
Preprint
Full-text available
Merging at highway on-ramps while interacting with other human-driven vehicles is challenging for autonomous vehicles (AVs). An efficient route to this challenge requires exploring and then exploiting knowledge of the interaction process from demonstrations by humans. However, it is unclear what information (or the environment states) is utilized b...
Article
Full-text available
Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driverʼ s abilities to control. The human driver, as an essential agent in the driver-vehicle shared control systems, should be precisely modeled regarding their cognitive processes, control strategies, and decision-maki...
Preprint
Full-text available
Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver's abilities to control. The human driver, as an essential agent in the driver-vehicle shared control systems, should be precisely modeled regarding their cognitive processes, control strategies, and decision-makin...
Preprint
Full-text available
Understanding multi-vehicle interactive behaviors with temporal sequential observations is crucial for autonomous vehicles to make appropriate decisions in an uncertain traffic environment. On-demand similarity measures are significant for autonomous vehicles to deal with massive interactive driving behaviors by clustering and classifying diverse s...
Preprint
Full-text available
Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowledge of specific scenarios with predefined models, limiting their adaptive capabilities. This paper describes a Bayesian nonparametric approach that leverages continuous (i...
Article
Classification and analysis of driving behaviors offer in-depth knowledge to make an efficient decision for autonomous vehicles. This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising two layers: feature re...
Article
Evaluating the similarity levels of driving behavior plays a pivotal role in driving style classification and analysis, thus benefiting the design of human-centric driver assistance systems. This paper presents a novel framework capable of quantitatively measuring the similarity of driving behaviors for human based on driving primitives, i.e., the...
Article
Semantic understanding of multi-vehicle interaction patterns at intersections play a pivotal role in proper decision-making of autonomous vehicles. This paper presents a flexible framework to automatically extract these interaction patterns from observed temporal sequences based on driving primitives. A Bayesian nonparametric approach is developed...
Article
Individual driver's driving behavior plays a pivotal role in personalized driver assistance systems. Gaussian mixture models (GMM) have been widely used to fit driving data, but unsuitable for capturing the data with a long-tailed distribution. Though the generalized GMM (GGMM) could overcome this fitting issue to some extent, it still cannot handl...
Preprint
Predicting surrounding vehicle behaviors are critical to autonomous vehicles when negotiating in multi-vehicle interaction scenarios. Most existing approaches require tedious training process with large amounts of data and may fail to capture the propagating uncertainty in interaction behaviors. The multi-vehicle behaviors are assumed to be generat...
Preprint
Full-text available
Neural processes (NPs) learn stochastic processes and predict the distribution of target output adaptively conditioned on a context set of observed input-output pairs. Furthermore, Attentive Neural Process (ANP) improved the prediction accuracy of NPs by incorporating attention mechanism among contexts and targets. In a number of real-world applica...
Preprint
Generating multi-vehicle interaction scenarios can benefit motion planning and decision making of autonomous vehicles when on-road data is insufficient. This paper presents an efficient approach to generate varied multi-vehicle interaction scenarios that can both adapt to different road geometries and inherit the key interaction patterns in real-wo...
Conference Paper
Full-text available
Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions. This paper presents a general framework to gain insights into intricate multi-vehicle interaction patterns from bird's-eye view traffic videos. We adopt a Gaussi...
Article
Safely passing through unsignalized intersections (USI) in urban area is challenging for autonomous vehicles due to high uncertainties of surrounding engaged human-driven vehicles. In order to achieve this, various variables have been selected to estimate and predict the surrounding human driver's behavior. However, it is still not fully clear what...
Article
Fast recognition of a driver’s decision-making style when changing lanes plays a pivotal role in a safety-oriented and personalized vehicle control system design. This article presents a time-efficient recognition method by integrating $ k$ -means clustering ( $ k$ -MC) with the K-nearest neighbor (KNN) algorithm, called $ k$ MC-KNN. Mathemati...
Preprint
One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assumption deviates from actual human behaviors under ambiguity. Risk-sensitive inverse reinforcement learning (RS-IRL) bridges such gap by assuming that humans act according...
Preprint
Full-text available
Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions. This paper presents a general framework to gain insights into intricate multi-vehicle interaction patterns from bird's-eye view traffic videos. We adopt a Gaussi...
Preprint
Full-text available
Autonomous vehicles (AV) are expected to navigate in complex traffic scenarios with multiple surrounding vehicles. The correlations between road users vary over time, the degree of which, in theory, could be infinitely large, and thus posing a great challenge in modeling and predicting the driving environment. In this research, we propose a method...
Article
To safely and efficiently change lanes among human drivers, autonomous vehicles (AVs) should make human-like decisions and seamlessly cooperate with surrounding vehicles. Both overaggressive and over-conservative cut-in maneuvers will have adverse effects on traffic efficiency and safety. However, it is still not entirely clear how much influence o...
Article
Full-text available
Purpose Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify L...
Preprint
Full-text available
Fast recognizing driver's decision-making style of changing lanes plays a pivotal role in safety-oriented and personalized vehicle control system design. This paper presents a time-efficient recognition method by integrating k-means clustering (k-MC) with K-nearest neighbor (KNN), called kMC-KNN. The mathematical morphology is implemented to automa...
Article
Estimating driver's lane-change (LC) intent is very important to avoid traffic accident caused by improper LC maneuvers. This paper proposes a lane-change Bayesian networks (LCBN) incorporated with a Gaussian mixture model (GMM), termed as LCBN-GMM, to estimate driver's LC intent considering drivers' driving style over varying scenarios. According...
Article
Deep learning techniques have been widely used in autonomous driving community for the purpose of environment perception. Recently, it starts being adopted for learning end-to-end controllers for complex driving scenarios. However, the complexity and nonlinearity of the network architecture limits its interpretability to understand driving scenario...
Preprint
Full-text available
Generating multi-vehicle trajectories analogous to these in real world can provide reliable and versatile testing scenarios for autonomous vehicle. This paper presents an unsupervised learning framework to achieve this. First, we implement variational autoencoder (VAE) to extract interpretable and controllable representatives of vehicle encounter t...
Article
Full-text available
Developing an automated vehicle, that can handle the complicated driving scenarios and appropriately interact with other road users, requires the ability to semantically learn and understand the driving environment, oftentimes, based on the analysis of massive amount of naturalistic driving data. An important paradigm that allows automated vehicles...
Preprint
Full-text available
Semantically understanding complex drivers' encountering behavior, wherein two or multiple vehicles are spatially close to each other, does potentially benefit autonomous car's decision-making design. This paper presents a framework of analyzing various encountering behaviors through decomposing driving encounter data into small building blocks, ca...
Article
Full-text available
Driving encounter classification and analysis can benefit autonomous vehicles to efficiently achieve a more smart decision. This paper presents an unsupervised learning framework to classify a wide range of driving encounters which compose of a pair of vehicles' GPS trajectories ordered by time. First, we develop five specific approaches, through i...
Preprint
Full-text available
Driving encounter classification and analysis can benefit autonomous vehicles to efficiently achieve a more smart decision. This paper presents an unsupervised learning framework to classify a wide range of driving encounters which compose of a pair of vehicles' GPS trajectories ordered by time. First, we develop five specific approaches, through i...
Article
Full-text available
The performance of energy management systems in hybrid electric vehicles (HEVs) is highly related to drivers' driving style. This paper proposes a driving style-oriented adaptive equivalent consumption minimization strategy (AECMS-style) in order to improve fuel economy for HEVs. For this purpose, firstly, a statistical pattern recognition approach...
Article
Full-text available
Misunderstanding of driver correction behaviors (DCB) is the primary reason for false warnings of lane-departure-prediction systems. We propose a learning-based approach to predicting unintended lane-departure behaviors (LDB) and the chance for drivers to bring the vehicle back to the lane. First, in this approach, a personalized driver model for l...
Conference Paper
A new driver model adaptation (DMA) method is proposed in this paper to help the model adaptation between different individual drivers. This method is based on transfer learning which can improve the DMA process at data level. The Gaussian mixture model (GMM)-based method is used to model the steering behaviour of drivers during the overtaking mano...
Preprint
Full-text available
LiDARs plays an important role in self-driving cars and its configuration such as the location placement for each LiDAR can influence object detection performance. This paper aims to investigate an optimal configuration that maximizes the utility of on-hand LiDARs. First, a perception model of LiDAR is built based on its physical attributes. Then a...
Preprint
Full-text available
A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV). However, the heterogeneities of databases in size, structure and driving context make existing datasets practically ineffective due to a lack of uniform frameworks and searchable indexes. In order to overcome these limitations on exi...
Article
Full-text available
Deep understanding of driving encounters could help self-driving cars make appropriate decisions when driving in complex settings with surrounding vehicles engaged. This paper develops an unsupervised classifier to group naturalistic driving encounters into several distinguishable clusters by combining an auto-encoder with a k-means clustering (AE-...
Article
Full-text available
Driving style recognition plays a crucial role in eco-driving, road safety, and intelligent vehicle control. This paper proposes a statistical-based recognition method to deal with driver behaviour uncertainty in driving style recognition. First, we extract discriminative features using the conditional kernel density function to characterize path-f...
Article
Full-text available
Accurately predicting and inferring a driver's decision to brake is critical for designing warning systems and avoiding collisions. In this paper we focus on predicting a driver's intent to brake in car-following scenarios from a perception-decision-action perspective according to his/her driving history. A learning-based inference method, using on...
Article
Full-text available
Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidd...
Article
Full-text available
Supervised learning approaches are widely used for driving style classification; however, they often require a large amount of labeled training data, which is usually scarce in a real-world setting. Moreover, it is time-consuming to manually label huge amounts of driving data due to uncertainties of driver behavior and variances among the data anal...
Article
Full-text available
Big data has shown its uniquely powerful ability to reveal, model, and understand driver behaviors. The amount of data affects the experiment cost and conclusions in the analysis. Insufficient data may lead to inaccurate models while excessive data waste resources. For projects that cost millions of dollars, it is critical to determine the right am...
Article
Full-text available
Personalized driver models play a key role in the development of advanced driver assistance systems and automated driving systems. Traditionally, physical-based driver models with fixed structures usually lack the flexibility to describe the uncertainties and high non-linearity of driver behaviors. In this paper, two kinds of learning-based car-fol...
Article
Full-text available
Evaluating the effectiveness and benefits of driver assistance systems is essential for improving the system performance. In this paper, we propose an efficient evaluation method for a semi-autonomous lane departure correction system. To achieve this, we apply a bounded Gaussian mixture model to describe drivers' stochastic lane departure behavior...
Article
Evaluating the effectiveness and benefits of driver assistance systems is crucial for improving the system performance. In this paper, we propose a novel framework for testing and evaluating lane departure correction systems at a low cost by using lane departure events reproduced from naturalistic driving data. First, 529,096 lane departure events...
Conference Paper
A Bayesian network decision-making method is proposed by combining driver’s eye tracking data and vehicle-based data together to identify driver lane-changing intents. First, experiments are conducted in a driving simulator with eye-tracker device to obtain the data when the subject driver makes lane-changing maneuvers. Second, collected data are a...
Article
To improve vehicle path-following performance and reduce driver workload, a human-centered feed-forward control (HCFC) system for a vehicle steering system is proposed. To be specific, a novel dynamic control strategy for steering ratio of vehicle steering systems that treats vehicle speed, lateral deviation, yaw error and steering angle as the inp...
Conference Paper
Full-text available
A rapid pattern-recognition approach to characterize driver’s curve-negotiating behavior is proposed. To shorten the recognition time and improve the recognition of driving styles, a k-means clustering-based support vector machine (kMC-SVM) method is developed and used for classifying drivers into two types: aggressive and moderate. First, vehicle...
Conference Paper
A rapid pattern-recognition approach to characterize driver's curve-negotiating behavior is proposed. To shorten the recognition time and improve the recognition of driving styles, a k-means clustering-based support vector machine ( kMC-SVM) method is developed and used for classifying drivers into two types: aggressive and moderate. First, vehicle...
Article
Full-text available
To improve the vehicle's path-following performance and reduce the driver's work load, a control strategy for a vehicle steering system with an embedded driver model is presented based on the driver's steering behavior. Then, a new driver model of path-following is proposed according to general driver behaviors and is integrated into a vehicle bicy...