Autonomous vehicles are more likely to be accepted if they drive accurately, comfortably, but also similar to how human drivers would. This is especially true when autonomous and human-driven vehicles need to share the same road. The main research focus thus far, however, is still on improving driving accuracy only. This paper formalizes the three concerns with the aim of accurate, comfortable and human-like driving. Three contributions are made in this paper. First, numerical map data from HERE Technologies are employed for more accurate driving; a set of map features which are believed to be relevant to driving are engineered to navigate better. Second, the learning procedure is improved from a pointwise prediction to a sequence-based prediction and passengers' comfort measures are embedded into the learning algorithm. Finally, we take advantage of the advances in adversary learning to learn human-like driving; specifically, the standard L1 or L2 loss is augmented by an adversary loss which is based on a discriminator trained to distinguish between human driving and machine driving. Our model is trained and evaluated on the Drive360 dataset, which features 60 hours and 3000 km of real-world driving data. Extensive experiments show that our driving model is more accurate, more comfortable and behaves more like a human driver than previous methods. The resources of this work will be released on the project page.
. A large body of work has studied human driving styles @cite_43 @cite_42 . Statistical approaches were employed to evaluate human drivers and to suggest improvements @cite_18 @cite_39 . This line of research inspired us to ask whether . Human-like driving is hard to quantify. Fortunately, recent advances in adversarial learning provide the tools to extract the of human-like driving, using it to adjust machine driving so that it becomes more human-like. Some work has studied human-like motion planning of autonomous cars, but it was constrained to simulated scenarios @cite_41 @cite_19 . The closest work to ours was done by @cite_44 where a set of manually-crafted features are used to characterize human driving style. Our method learns the features directly from the data using adversarial neural networks.
@cite_18 @cite_41 @cite_42 @cite_39 @cite_44 @cite_43 @cite_19
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We evaluate a mobile application that assesses driving behavior based on in-vehicle acceleration measurements and gives corresponding feedback to drivers. In the insurance business, such applications have recently gained traction as a viable alternative to the monitoring of drivers via "black boxes" installed in vehicles, which lacks interaction opportunities and is perceived as privacy intrusive by policyholders. However, pose uncertainty and other noise-inducing factors make smartphones potentially less reliable as sensor platforms. We therefore compare critical driving events generated by a smartphone with reference measurements from a vehicle-fixed IMU in a controlled field study. The study was designed to capture driver variability under real-world conditions, while minimizing the influence of external factors. We find that the mobile measurements tend to overestimate critical driving events, possibly due to deviation from the calibrated initial device pose. While weather and daytime do not appear to influence event counts, road type is a significant factor that is not considered in most current state-of-the-art implementations. A framework for modeling driver behavior within driving simulators is described in this paper. This framework serves as a basis for building human- like driving behavior models for autonomous vehicles operating within the virtual environment of a driving simulator. The framework consists of four units, the Perception Unit, the Emotions Unit, the Decision- making Unit (DMU), and the Decision- implementation Unit (DIU). The Perception Unit defines how the model perceives its environment in local and global terms. The Emotions Unit defines how the model responds emotionally to its environment. The DMU investigates the environment for possible actions that might potentially serve the model's emotional demands. And finally the DIU tries to implement these decisions when a traffic condition, perceived as safe enough for such an implementation, emerges. Each of these units has its own set of fuzzy variables and fuzzy ifthen rules. Any driving model, that is based on this framework, should provide membership function parameters for these fuzzy variables in accordance with the category of human driving behavior this model is targeting. Our framework addresses decision making and implementation at the maneuvering and operational levels of the driving task. Decisions at the planning level are addressed through a script- based traffic controller. The present model is limited to simulating human behaviors when driving in a two- lane rural environment. In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced. In this paper, we develop a smart phone-based driving behavior evaluation system, named Join Driving, which helps drivers notice how aggressive their driving behaviors are and be aware of the riding comfort level of passengers. The proposed evaluation system is made of two parts: driving events detection and evaluation part and riding comfort level evaluation part. In driving events detection and evaluation part, the proposed system, Join Driving, first presents a model to detect drivers' driving events, based on the data collected from the acceleration, orientation and GPS sensors in smart phones. Then, based on the detected drivers' driving events, Join Driving implements a novel scoring mechanism to quantitatively evaluate how aggressive these driving events are. In riding comfort level evaluation part, the proposed system gives the specific scores to rate passengers' riding comfort level based on ISO 2631. Finally, several practical experiments are conducted to evaluate the effectiveness of the proposed scoring system. It is expected that autonomous vehicles capable of driving without human supervision will be released to market within the next decade. For user acceptance, such vehicles should not only be safe and reliable, they should also provide a comfortable user experience. However, individual perception of comfort may vary considerably among users. Whereas some users might prefer sporty driving with high accelerations, others might prefer a more relaxed style. Typically, a large number of parameters such as acceleration profiles, distances to other cars, speed during lane changes, etc., characterize a human driver's style. Manual tuning of these parameters may be a tedious and error-prone task. Therefore, we propose a learning from demonstration approach that allows the user to simply demonstrate the desired style by driving the car manually. We model the individual style in terms of a cost function and use feature-based inverse reinforcement learning to find the model parameters that fit the observed style best. Once the model has been learned, it can be used to efficiently compute trajectories for the vehicle in autonomous mode. We show that our approach is capable of learning cost functions and reproducing different driving styles using data from real drivers. This paper considers a comprehensive and collaborative project to collect large amounts of driving data on the road for use in a wide range of areas of vehicle-related research centered on driving behavior. Unlike previous data collection efforts, the corpora collected here contain both human and vehicle sensor data, together with rich and continuous transcriptions. While most efforts on in-vehicle research are generally focused within individual countries, this effort links a collaborative team from three diverse regions (i.e., Asia, American, and Europe). Details relating to the data collection paradigm, such as sensors, driver information, routes, and transcription protocols, are discussed, and a preliminary analysis of the data across the three data collection sites from the U.S. (Dallas), Japan (Nagoya), and Turkey (Istanbul) is provided. The usability of the corpora has been experimentally verified with a Cohen's kappa coefficient of 0.74 for transcription reliability, as well as being successfully exploited for several in-vehicle applications. Most importantly, the corpora are publicly available for research use and represent one of the first multination efforts to share resources and understand driver characteristics. Future work on distributing the corpora to the wider research community is also discussed. Autonomous vehicles are perhaps the most encountered element in a driving simulator. Their effect on the realism of the simulator is critical. For autonomous vehicles to contribute positively to the realism of the hosting driving simulator, they need to have a realistic appearance and, possibly more importantly, realistic behavior. Addressed is the problem of modeling realistic and humanlike behaviors on simulated highway systems by developing an abstract framework that captures the details of human driving at the microscopic level. This framework consists of four units that together define and specify the elements needed for a concrete humanlike driving model to be implemented within a driving simulator. These units are the perception unit, the emotions unit, the decision-making unit, and the decision-implementation unit. Realistic models of humanlike driving behavior can be built by implementing the specifications set by the driving framework. Four humanlike driving models have been implemented on the basis of the driving framework: (a) a generic normal driving model, (b) an aggressive driving model, (c) an alcoholic driving model, and (d) an elderly driving model. These driving models provide experiment designers with a powerful tool for generating complex traffic scenarios in their experiments. These behavioral models were incorporated along with three-dimensional visual models and vehicle dynamics models into one entity, which is the autonomous vehicle. Subjects perceived the autonomous vehicles with the described behavioral models as having a positive effect on the realism of the driving simulator. The erratic driving models were identified correctly by the subjects in most cases.