Högskolan i Skövde

Every year worldwide more than one million people die and a further 50 million people are injured in traffic accidents. Therefore, the development of car safety features that actively support the driver in preventing accidents, is of utmost importance to reduce the number of injuries and fatalities. However, to establish this support it is necessary that the advanced driver assistance system (ADAS) understands the driver’s intended behavior in advance. The growing variety of sensors available for vehicles together with improved computer vision techniques, hence led to increased research directed towards inferring the driver’s intentions. This article reviews 64 driver intention recognition studies with regard to the maneuvers considered, the driving features included, the AI methods utilized, the achieved performance within the presented experiments, and the open challenges identified by the respected researchers. The article provides a high level analysis of the current technology readiness level of driver intention recognition technology to address the challenges to enable reliable driver intention recognition, such as the system architecture, implementation, and the purpose of the technology.

Real-world applications of artificial intelligence that can potentially harm human beings should be able to express uncertainty about the made predictions. Probabilistic deep learning (DL) methods (e.g., variational inference [VI], VI last layer [VI-LL], Monte-Carlo [MC] dropout, stochastic weight averaging - Gaussian [SWA-G], and deep ensembles) can produce a predictive uncertainty but require expensive MC sampling techniques. Therefore, we evaluated if the probabilistic DL methods are uncertain when making incorrect predictions for an open-source driver intention recognition dataset and if a surrogate DL model can reproduce the uncertainty estimates. We found that all probabilistic DL methods are significantly more uncertain when making incorrect predictions at test time, but there are still instances where the models are very certain but completely incorrect. The surrogate DL models trained on the MC dropout and VI uncertainty estimates were capable of reproducing a significantly higher uncertainty estimate when making incorrect predictions.

Traffic fatalities remain among the leading death causes worldwide. To reduce this figure, car safety is listed as one of the most important factors. To actively support human drivers, it is essential for advanced driving assistance systems to be able to recognize the driver's actions and intentions. Prior studies have demonstrated various approaches to recognize driving actions and intentions based on in-cabin and external video footage. Given the performance of self-supervised video pre-trained (SSVP) Video Masked Autoencoders (VMAEs) on multiple action recognition datasets, we evaluate the performance of SSVP VMAEs on the Honda Research Institute Driving Dataset for driver action recognition (DAR) and on the Brain4Cars dataset for driver intention recognition (DIR). Besides the performance, the application of an artificial intelligence system in a safety-critical environment must be capable to express when it is uncertain about the produced results. Therefore, we also analyze uncertainty estimations produced by a Bayes-by-Backprop last-layer (BBB-LL) and Monte-Carlo (MC) dropout variants of an VMAE. Our experiments show that an VMAE achieves a higher overall performance for both offline DAR and end-to-end DIR compared to the state-of-the-art. The analysis of the BBB-LL and MC dropout models show higher uncertainty estimates for incorrectly classified test instances compared to correctly predicted test instances.

Driver intention recognition (DIR) methods mostly rely on deep neural networks (DNNs). To use DNNs in asafety-critical real-world environment it is essential to quantify how confident the model is about the producedpredictions. Therefore, this study evaluates the performance and calibration of a temporal convolutionalnetwork (TCN) for multiple probabilistic deep learning (PDL) methods (Bayes-by-Backprop, Monte-Carlodropout, Deep ensembles, Stochastic Weight averaging - Gaussian, Multi SWA-G, cyclic Stochastic GradientHamiltonian Monte Carlo). Notably, we formalize an approach that combines optimization-based pre-trainingwith Hamiltonian Monte-Carlo (PT-HMC) sampling, aiming to leverage the strengths of both techniques. Ouranalysis, conducted on two pre-processed open-source DIR datasets, reveals that PT-HMC not only matchesbut occasionally surpasses the performance of existing PDL methods. One of the remaining challenges thatprohibits the integration of a PDL-based DIR system into an actual car is the computational requirements toperform inference. Therefore, future work could focus on optimizing PDL methods to be more computationallyefficient without sacrificing performance or the ability to estimate uncertainties.

Driver intention recognition (DIR) studies increasingly rely on deep neural networks. Deep neural networks have achieved top performance for many different tasks. However, apart from image classifications and semantic segmentation for mobile phones, it is not a common practice for components of advanced driver assistance systems to explicitly analyze the complexity and performance of the network’s architecture. Therefore, this paper applies neural architecture search to investigate the effects of the deep neural network architecture on a real-world safety critical application with limited computational capabilities. We explore a pre-defined search space for three deep neural network layer types that are capable to handle sequential data (a long-short term memory, temporal convolution, and a time-series transformer layer), and the influence of different data fusion strategies on the driver intention recognition performance. A set of eight search strategies are evaluated for two driver intention recognition datasets. For the two datasets, we observed that there is no search strategy clearly sampling better deep neural network architectures. However, performing an architecture search improves the model performance compared to the original manually designed networks. Furthermore, we observe no relation between increased model complexity and better driver intention recognition performance. The result indicate that multiple architectures can yield similar performance, regardless of the deep neural network layer type or fusion strategy. However, the optimal complexity, layer type and fusion remain unknown upfront.

Anticipating the behavior of other road users is critical for safe driving. To anticipate the behavior of other road users in a timely manner, it is essential to recognize their intentions. Although artificial intelligence (AI)-based intention recognition systems for traffic scenarios have advanced significantly, their performance relative to human road user intention recognition (RUIR) remains largely unexplored. To address this gap, we conducted an experiment comparing the RUIR performance of human participants and a state-of-the-art end-to-end video recognition AI model on a set of 25 video scenarios. The selected scenarios offered a balanced representation of various road user types and a range of intention maneuvers. Among human participants (N=161), we found no statistically significant differences in RUIR performance with respect to age, self-perceived driving skill, annual driven kilometers, or years of driving experience. However, the average human participant exhibited slightly lower RUIR performance than the AI models.