Driver workload estimation using a novel hybrid method of error reduction ratio causality and support vector machine

Measuring driver workload is of great significance for improving the understanding of driver behaviours and supporting the improvement of advanced driver assistance systems technologies. In this paper, a novel hybrid method for measuring driver workload estimation for real-world driving data is proposed. Error reduction ratio causality, a new nonlinear causality detection approach, is being proposed in order to assess the correlation of each measured variable to the variation of workload. A full model describing the relationship between the workload and the selected important measurements is then trained via a support vector regression model. Real driving data of 10 participants, comprising 15 measured physiological and vehicle-state variables are used for the purpose of validation. Test results show that the developed error reduction ratio causality method can effectively identify the important variables that relate to the variation of driver workload, and the support vector regression based model can successfully and robustly estimate workload

A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles

Deep reinforcement learning (DRL) is becoming a prevalent and powerful methodology to address the artificial intelligent problems. Owing to its tremendous potentials in self-learning and self-improvement, DRL is broadly serviced in many research fields. This article conducted a comprehensive comparison of multiple DRL approaches on the freeway decision-making problem for autonomous vehicles. These techniques include the common deep Q learning (DQL), double DQL (DDQL), dueling DQL, and prioritized replay DQL. First, the reinforcement learning (RL) framework is introduced. As an extension, the implementations of the above mentioned DRL methods are established mathematically. Then, the freeway driving scenario for the automated vehicles is constructed, wherein the decision-making problem is transferred as a control optimization problem. Finally, a series of simulation experiments are achieved to evaluate the control performance of these DRL-enabled decision-making strategies. A comparative analysis is realized to connect the autonomous driving results with the learning characteristics of these DRL techniques.Comment: 11 pages, 10 figure

Cyber-physical system based optimization framework for intelligent powertrain control

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented. Simulation-based parameter optimizations are carried out according to the objective functions. Simulation results show that an electric powertrain with intelligent controller can perform its tasks well under sport, eco, and normal driving modes. The vehicle further improves overall performance in vehicle dynamics, ride comfort, and energy efficiency. The results validate the feasibility and effectiveness of the proposed CPS-based optimization framework, and demonstrate its advantages over a baseline benchmark

Analysis of autopilot disengagements occurring during autonomous vehicle testing

In present-day highly-automated vehicles, there are occasions when the driving system disengages and the human driver is required to take-over. This is of great importance to a vehicle U+02BC s safety and ride comfort. In the U.S state of California, the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing. On 1 January 2016, seven manufacturers submitted their first disengagement reports: Bosch, Delphi, Google, Nissan, Mercedes-Benz, Volkswagen, and Tesla Motors. This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover, as this is potentially useful in improving the Society of Automotive Engineers U+0028 SAE U+0029 Level 2 and Level 3 automation technologies. Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail. The mechanisms and time taken for take-over transition occurred in disengagements are studied. Finally, recommendations for OEMs, manufacturers, and government organizations are also discussed

A distributionally robust optimization model for vehicle platooning under stochastic disturbances

Inspired by connected and autonomous driving technologies, this paper proposes a closed-loop Distributionally Robust Model Predictive Control (DRMPC) method to address the problem of longitudinal platoon control disturbed by V2V communication noise. In particular, a Model Predictive Control (MPC)-based vehicle platoon control model subject to stochastic disturbances is first developed. Vehicle control and state are imposed with probabilistic chance constraints, and a state feedback structure is designed to ensure the stability of the platoon system, which poses a significant challenge to the platoon control system. To solve this computationally intractable DRMPC model, a Ball ambiguity set is constructed using the characteristic information (expectation and variance) of random variables. The original DRMPC model is reformulated into a computationally tractable robust counterpart approximation framework. Furthermore, the recursive feasibility of the proposed DRMPC and the string stability of the platoon vehicles are demonstrated by introducing an initialization strategy for nominal states. Finally, a simulation study in a platooning system consisting of six vehicles is performed to verify the validity of the DRMPC model under stochastic V2V noise disturbances

DA-RDD: toward domain adaptive road damage detection across different countries

Recent advances on road damage detection relies on a large amount of labeled data, whilst collecting pavement image is labor-intensive and time-consuming. Unsupervised Domain Adaptation (UDA) provides a promising solution to adapt a source domain to the target domain, however, cross-domain crack detection is still an open problem. In this paper, we propose domain adaptive road damage detection termed as DA-RDD, by incorporating image-level with instance-level feature alignment for domain-invariant representation learning in an adversarial manner. Specifically, importance weighting is introduced to evaluate the intermediate samples for image-level alignment between domains, and we aggregate RoI-wise feature with multi-scale contextual information to recover the crack details for progressive domain alignment at instance level. Additionally, a large-scale road damage dataset (based on Road Damage Dataset 2020 (RDD2020)) named as RDD2021 is constructed with 100k synthetic labeled distress images. Extensive experimental results on damage detection across different countries demonstrate the universality and superiority of DA-RDD, and empirical studies on RDD2021 further claim its effectiveness and advancement. To our best knowledge, it is the first time to investigate domain adaptative pavement crack detection, and we expect the contributions in this work would facilitate the development of generalized road damage detection in the future

V2VFormer: vehicle-to-vehicle cooperative perception with spatial-channel transformer

Collaborative perception aims for a holistic perceptive construction by leveraging complementary information from nearby connected automated vehicle (CAV), thereby endowing the broader probing scope. Nonetheless, how to aggregate individual observation reasonably remains an open problem. In this paper, we propose a novel vehicle-to-vehicle perception framework dubbed V2VFormer with Tr ansformer-based Co llaboration ( CoTr ). Specifically. it re-calibrates feature importance according to position correlation via Spatial-Aware Transformer ( SAT ), and then performs dynamic semantic interaction with Channel-Wise Transformer ( CWT ). Of note, CoTr is a light-weight and plug-in-play module that can be adapted seamlessly to the off-the-shelf 3D detectors with an acceptable computational overhead. Additionally, a large-scale cooperative perception dataset V2V-Set is further augmented with a variety of driving conditions, thereby providing extensive knowledge for model pretraining. Qualitative and quantitative experiments demonstrate our proposed V2VFormer achieves the state-of-the-art (SOTA) collaboration performance in both simulated and real-world scenarios, outperforming all counterparts by a substantial margin. We expect this would propel the progress of networked autonomous-driving research in the future