Highway Driving Research Articles

On-the-Go Automated Break Recommendation for Stress Avoidance during Highway Driving

Continuous cab driving is considered a highly stressful job, although drivers often ignore the stress. Taking a break from manual driving or transferring the control to another driver to release the stress would be an easy, intuitive solution, although the challenge is to detect the driving stress while the trip is going on. As driving stress depends on multiple diverse environmental and affective features, we, in this article, develop a novel assistive system, SmartHalt , which continuously senses the driving environment (such as road type, congestion, driver’s driving pattern) and then utilizes a spatial time series model of the driving environment with a deep learning framework to predict whether the driver will be stressed while on the trip. The model also considers the personality traits of the drivers along with the spatio-temporal features to differentiate the impact of stress on the driving behavior for different drivers and recommends taking a break soon before the driving behavior drops below a critical level. A thorough analysis of the model over seven different drivers for a 10 month-long experiment over 204,871 km of driving data reveals that the proposed approach can significantly improve driving behavior by recommending a driving break at proper times. Following the recommendation by SmartHalt improves the driving score by ≈50% and reduces the number of driving offenses by ≈50%. SmartHalt can help develop advanced driving assisting system (ADAS) platforms that understand the affective states of the driver and thus can be helpful for semi-autonomous driving environments for effective driver-vehicle interactions.

Heat transfer and economic characteristics of direct oil cooling for electric traction motor under driving cycles

Currently, the energy density of electric traction motor is continuously increasing to match the performance and reliability of electric vehicles with IC engine vehicles. The commercially employed water jacket cooling fails to achieve the desired efficiency of high energy density electric traction motor. Direct oil cooling is one of potential cooling strategies to replace the conventional cooling method for achieving the desired thermal management of next generation electric traction motors. However, the database to commercialize the direct oil cooling based on thermal management and economic aspects is not enriched sufficiently and thus extensive research studies need to be conducted in this field. Therefore, the heat transfer and economic characteristics of direct oil cooling for electric traction motor are investigated considering different cooling oils and driving duty cycles in the present work. The integrated electromagnetic-thermal numerical model is developed using ANSYS Motor-Cad commercial software to simulate the heat transfer and economic characteristics. The reliability of coupled numerical model is assured by validating the simulated results with experimental and reference data in error range of ± 5 %. The winding temperature, heat transfer coefficient, expected insulation lifetime, levelized cost of energy (LCOE), and net cumulative savings are evaluated as heat transfer and economic characteristics. Three driving duty cycles namely, UDDS (Urban Dynamometer Driving Schedule), NEDC (New European Driving Cycle) and US06 (Highway Driving Cycle) and transmission oil, shell thermal oil and Novec dielectric oil are investigated. The performance of electric traction motor with direct oil cooling is superior compared to that with water jacket cooling. The direct cooling with Novec dielectric oil shows improved heat transfer and economic characteristics compared to transmission oil and shell thermal oil. The direct cooling with Novec dielectric oil restricts the maximum winding temperature within 80.21 °C, 67.65 °C and 116.82 °C under driving cycles of UDDS, NEDC and US06, respectively. In addition, the enhanced insulation lifetime, heat transfer coefficient, LCOE and net cumulative savings of 7.91 × 105h, 23036.73 W/m2-K, 0.170 $/kWh and 47151.13 $, respectively are reflected for direct cooling with Novec dielectric oil.

Enhancing Highway Driving: High Automated Vehicle Decision Making in a Complex Multi-Body Simulation Environment

Automated driving is a promising development in reducing driving accidents and improving the efficiency of driving. This study focuses on developing a decision-making strategy for autonomous vehicles, specifically addressing maneuvers such as lane change, double lane change, and lane keeping on highways, using deep reinforcement learning (DRL). To achieve this, a highway driving environment in the commercial multi-body simulation software IPG Carmaker 11 version is established, wherein the ego vehicle navigates through surrounding vehicles safely and efficiently. A hierarchical control framework is introduced to manage these vehicles, with upper-level control handling driving decisions. The DDPG (deep deterministic policy gradient) algorithm, a specific DRL method, is employed to formulate the highway decision-making strategy, simulated in MATLAB software. Also, the computational procedures of both DDPG and deep Q-network algorithms are outlined and compared. A set of simulation tests is carried out to evaluate the effectiveness of the suggested decision-making policy. The research underscores the advantages of the proposed framework concerning its convergence rate and control performance. The results demonstrate that the DDPG-based overtaking strategy enables efficient and safe completion of highway driving tasks.

Towards Robust Decision-Making for Autonomous Highway Driving Based on Safe Reinforcement Learning.

Reinforcement Learning (RL) methods are regarded as effective for designing autonomous driving policies. However, even when RL policies are trained to convergence, ensuring their robust safety remains a challenge, particularly in long-tail data. Therefore, decision-making based on RL must adequately consider potential variations in data distribution. This paper presents a framework for highway autonomous driving decisions that prioritizes both safety and robustness. Utilizing the proposed Replay Buffer Constrained Policy Optimization (RECPO) method, this framework updates RL strategies to maximize rewards while ensuring that the policies always remain within safety constraints. We incorporate importance sampling techniques to collect and store data in a Replay buffer during agent operation, allowing the reutilization of data from old policies for training new policy models, thus mitigating potential catastrophic forgetting. Additionally, we transform the highway autonomous driving decision problem into a Constrained Markov Decision Process (CMDP) and apply our proposed RECPO for training, optimizing highway driving policies. Finally, we deploy our method in the CARLA simulation environment and compare its performance in typical highway scenarios against traditional CPO, current advanced strategies based on Deep Deterministic Policy Gradient (DDPG), and IDM + MOBIL (Intelligent Driver Model and the model for minimizing overall braking induced by lane changes). The results show that our framework significantly enhances model convergence speed, safety, and decision-making stability, achieving a zero-collision rate in highway autonomous driving.

State of Charge Estimation for Lithium Battery Based on Fractional Order Square Root Cubature Kalman Filter and Adaptive Multi-innovation Unscented Kalman Filter

Accurate state of charge (SOC) estimation of batteries is of great significance for electric vehicles. A SOC estimation method based on a fractional order square root cubature Kalman filter (FOSRCKF) and an adaptive multi-innovation unscented Kalman filter (AMIUKF) is proposed. The battery is modelled using fractional order calculus theory and the model parameters are identified by adaptive genetic algorithm. The FOSRCKF estimates the battery SOC, while the AMIUKF online updates the internal resistance in the model, and there exchanges information between two filters. The experimental results under the Urban Dynamometer Driving Schedule (UDDS) and the US06 Highway Driving Schedule show that the proposed method has lower SOC estimation error and lower terminal prediction error compared with the traditional SRCKF method based on integer order models, which demonstrates the effectiveness, accuracy and robustness of the proposed method.

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

AbstractAiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integer‐order models and offline parameter identification, this paper proposes a joint fractional‐order multi‐innovations unscented Kalman filter (FOUKF‐FOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singular‐value decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractional‐order unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multi‐innovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKF‐FOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the root‐mean‐square error is controlled within 0.8%. It is not difficult to find that the FOUKF‐FOMIUKF algorithm estimates SOC with higher accuracy and robustness.

3D Object Detection and Tracking Based on Lidar-Camera Fusion and IMM-UKF Algorithm Towards Highway Driving

3D Object Detection and Tracking Based on Lidar-Camera Fusion and IMM-UKF Algorithm Towards Highway Driving

A Hybrid Deep Reinforcement Learning and Optimal Control Architecture for Autonomous Highway Driving

Autonomous vehicles in highway driving scenarios are expected to become a reality in the next few years. Decision-making and motion planning algorithms, which allow autonomous vehicles to predict and tackle unpredictable road traffic situations, play a crucial role. Indeed, finding the optimal driving decision in all the different driving scenarios is a challenging task due to the large and complex variability of highway traffic scenarios. In this context, the aim of this work is to design an effective hybrid two-layer path planning architecture that, by exploiting the powerful tools offered by the emerging Deep Reinforcement Learning (DRL) in combination with model-based approaches, lets the autonomous vehicles properly behave in different highway traffic conditions and, accordingly, to determine the lateral and longitudinal control commands. Specifically, the DRL-based high-level planner is responsible for training the vehicle to choose tactical behaviors according to the surrounding environment, while the low-level control converts these choices into the lateral and longitudinal vehicle control actions to be imposed through an optimization problem based on Nonlinear Model Predictive Control (NMPC) approach, thus enforcing continuous constraints. The effectiveness of the proposed hierarchical architecture is hence evaluated via an integrated vehicular platform that combines the MATLAB environment with the SUMO (Simulation of Urban MObility) traffic simulator. The exhaustive simulation analysis, carried out on different non-trivial highway traffic scenarios, confirms the capability of the proposed strategy in driving the autonomous vehicles in different traffic scenarios.

Effect of Motion Cues on Highway Driving Performance in Simulated Driving

The motion system is a major component of a driving simulator. It provides motion cues in various directions with different degrees of freedom (DOF). Different DOF could influence response variables widely used in highway design safety evaluation conducted in simulators. Few studies, though, have comprehensively explored the effect of driving simulator motion cues on driving performance. The high-fidelity Tongji University Driving Simulator (TUDS) was used to separately compare five dependent variables: speed, speed change, lane deviation, longitudinal acceleration, and lateral acceleration. The Wilcoxon signed-rank test was used to examine the difference for each variable using 8-DOF and fixed-base motion system configurations for nine types of combined highway alignments. A subjective assessment of participants’ driving experience was conducted, and the chi-square test for each term was used to compare the difference in motion system fidelity and adverse reactions between the two configurations. Results show that: (1) speed and lateral acceleration are more likely to be significantly affected by motion cues than speed change, lane deviation, and longitudinal acceleration; (2) the horizontal curve with up-grade is the type of combined alignment where speed and lateral acceleration are mostly affected; (3) characteristics of the five variables in different combined alignments vary between the two simulators; (4) the 8-DOF motion system provides better perceived motion fidelity than the fixed-base system does, and the 8-DOF simulator causes less discomfort and tiredness in simulator sickness; and (5) the presence of motion cues is suggested for highways with complex combined curves when focusing on speed and lateral acceleration.

High-Level Decision Making for Automated Highway Driving via Behavior Cloning

Automated driving systems need to perform according to what human drivers expect in every situation. A different behavior can be wrongly interpreted by other human drivers and cause traffic problems, disturbances to other road participants, or in the worst case, an accident. In this paper, we propose a behavior cloning concept for learning high-level decisions from recorded trajectories of real traffic. We summarized and gave a clear definition of the main features that affect how humans make driving decisions. Some other approaches rely on complex neural networks where their decisions are impossible to understand. Due to the importance of the decision making module, we produce safe human-like behavior which is transparent to humans and easy to track. Furthermore, the learned policy is not overfitting to the limited training data and generalizes well to multi-lane scenarios with arbitrary speed limits and traffic density, which is strengthened by the successful application of merging policy on exiting. Simulation evaluations show that our learned policy is able to handle the intention uncertainty of surrounding agents, and provide human-like decisions in the sense of well-balanced behavior between efficiency, comfort, perceived safety, and politeness.

State-of-charge estimation for lithium-ion batteries based on dual extended Kalman filter

The strategy of estimating SOC by model is highly dependent on the accuracy of model. An SOC estimation method based on a dual extended Kalman fliter (DEKF) is proposed. One of the dual filters is employed to estimate the battery SOC, and the other is used to online identify the model parameters. The SOC estimation results by DEKF are compared with those by single EKF under the US06 Highway Driving Schedule test and Dynamic Stress Test (DST). The comparison results show that DEKF has higher SOC estimation and voltage prediction accuracy. Under the US06 and DST tests, the SOC mean absolute error (MAE) decreases from 2.57% and 3.00% to 1.18% and 1.56%, and the MAE of voltage prediction decreases from 54.6 mV and 38.3 mV to 43.9 mV and 29.1 mV, respectively. The SOC estimation and voltage prediction results demonstrate the effectiveness and accuracy of the proposed method.

Probabilistic Risk Metric for Highway Driving Leveraging Multi-Modal Trajectory Predictions

Road traffic safety has attracted increasing research attention, in particular in the current transition from human-driven vehicles to autonomous vehicles. Surrogate measures of safety are widely used to assess traffic safety but they typically ignore motion uncertainties and are inflexible in dealing with two-dimensional motion. Meanwhile, learning-based lane-change and trajectory prediction models have shown potential to provide accurate prediction results. We therefore propose a prediction-based driving risk metric for two-dimensional motion on multi-lane highways, expressed by the maximum risk value over different time instants within a prediction horizon. At each time instant, the risk of the vehicle is estimated as the sum of weighted risks over each mode in a finite set of lane-change maneuver possibilities. Under each maneuver mode, the risk is calculated as the product of three factors: lane-change maneuver mode probability, collision probability and expected crash severity. The three factors are estimated leveraging two-stage multi-modal trajectory predictions for surrounding vehicles: first a lane-change intention prediction module is invoked to provide lane-change maneuver mode possibilities, and then the mode possibilities are used as partial input for a multi-modal trajectory prediction module. Working with the empirical trajectory dataset highD and simulated highway scenarios, the proposed two-stage model achieves superior performance compared to a state-of-the-art prediction model. The proposed risk metric is computationally efficient for real-time applications, and effective to identify potential crashes earlier thanks to the employed prediction model.

Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter

Accurate state-of-charge (SOC) estimation of lithium batteries is of great significance for electric vehicles. In this paper, a combined estimation method of multi-innovation unscented Kalman filter (MIUKF) and fractional order square root cubature Kalman filter (FSRCKF) for lithium batteries is proposed. Firstly, the adaptive genetic algorithm (AGA) is applied to carry out offline parameter identification for the fractional order model (FOM) of a lithium battery under the Dynamic Stress Test (DST). Then, battery SOC is estimated by FSRCKF, while the Ohm internal resistance R0 of the fractional order battery model is estimated and updated by MIUKF in real time. The results show that MIUKF-FSRCKF is better than FSRCKF, FCKF and SRCKF in estimating the SOC of lithium batteries under the Federal Urban Driving Schedule (FUDS), Beijing Dynamic Stress Test (BJDST) and US06 Highway Driving Schedule tests, especially when R0 is inaccurate.

Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correction

It is challenging for a battery management system to estimate the State-Of-Charge (SOC) of batteries. A novel model-based method, using a Dual Extended Kalman Filtering algorithm (DEKF) and Back Propagation Neural Network (BPNN), is proposed to estimate and correct lithium-ion batteries. The results of acceptable SOC estimation are achieved using the DEKF to estimate the battery SOC and simultaneously update model parameters online, while the SOC estimation error is in real-time predicted by the trained BPNN. To further reduce the SOC estimation error, the SOC estimated by the DEKF is corrected by adding the predicted estimation error. The SOC estimation results between the original DEKF and BPNN-based updated DEKF methods under the Federal Urban Driving Schedule (FUDS), the Dynamic Stress Test (DST), the Beijing Dynamic Stress Test (BJDST) and the US06 Highway Driving Schedule are compared. Experimental results show that the SOC error reduces considerably after correcting the estimated SOC. The corrected SOC Root-Mean Square Errors (RMSEs) decrease by an average of seven times compared with the case of no correction. The constant current discharge test verifies the generality and robustness of the proposed method. The modification to the SOC estimation results using ordinary EKF under the above four sophisticated dynamic tests verifies the effectiveness of the proposed method.

Model Predictive Path-Planning Controller With Potential Function for Emergency Collision Avoidance on Highway Driving

Existing potential functions (PFs) utilized in autonomous vehicles mainly focus on solving the path-planning problems in some conventional driving scenarios; thus, their performance may not be satisfactory in the context of emergency obstacle avoidance. Therefore, we propose a novel model predictive path-planning controller (MPPC) combined with PFs to handle complex traffic scenarios (e.g., emergency avoidance when a sudden accident occurs). Specifically, to enhance the safety of the PFs, we developed an MPPC to handle an emergency case with a sigmoid-based safe passage embedded in the MPC constraints (SPMPC) with a specific triggering analysis algorithm on monitoring traffic emergencies. The presented PF-SPMPC algorithm was compiled in a comparative simulation study using MATLAB/Simulink and CarSim. The algorithm outperformed the latest PF-MPC approach to eliminate the severe tire oscillations and guarantee autonomous driving safety when handling the traffic emergency avoidance scenario.

Multi-Modal Model Predictive Control Through Batch Non-Holonomic Trajectory Optimization: Application to Highway Driving

Standard Model Predictive Control (MPC) or trajectory optimization approaches perform only a local search to solve a complex non-convex optimization problem. As a result, they cannot capture the multi-modal characteristic of human driving. A global optimizer can be a potential solution but is computationally intractable in a real-time setting. In this letter, we present a real-time MPC capable of searching over different driving modalities. Our basic idea is simple: we run several goal-directed parallel trajectory optimizations and score the resulting trajectories based on user-defined meta cost functions. This allows us to perform a search over several locally optimal motion plans. Although conceptually straightforward, realizing this idea in real-time with existing optimizers is highly challenging from technical and computational standpoints. With this motivation, we present a novel batch non-holonomic trajectory optimization whose underlying matrix algebra is easily parallelizable across problem instances and reduces to computing large batch matrix-vector products. This structure, in turn, is achieved by deriving a linearization-free multi-convex reformulation of the non-holonomic kinematics and collision avoidance constraints. We extensively validate our approach using both synthetic and real data sets (NGSIM) of traffic scenarios. We highlight how our algorithm automatically takes lane-change and overtaking decisions based on the defined meta cost function. Our batch optimizer achieves trajectories with lower meta cost, up to 6x faster than competing baselines.

Lightweight Deep Learning for Road Environment Recognition

With recent developments in the field of autonomous driving, recognition algorithms for road environments are being developed very rapidly. Currently, most of the network models have good recognition rates, but as the accuracy rate increases, the models become more complex and thus lack real-time performance. Therefore, there is an urgent need to propose a lightweight recognition system for road environments to assist autonomous driving. We propose a lightweight road environment recognition system with two different detection routes based on the same backbone network for objects and lane lines. The proposed approach uses MobileNet as the backbone network to acquire the feature layer, and our improved YOLOv4 and U-Net allows the number of parameters of the model to be greatly reduced and combined with the improved attention mechanism. The lightweight residual convolutional attention network (LRCA-Net) proposed in this work allows the network to adaptively pay attention to the feature details that need attention, which improves the detection accuracy. Finally, the object detection model and the lane line detection model of this lightweight road environment detection system evaluated on the PASCAL VOC dataset and the Highway Driving dataset show that their mAP and mIoU reach 93.2% and 93.3%, respectively, achieving excellent performance compared to other methods.

State-of-Charge Estimation for Lithium-Ion Batteries Based on Temperature-Based Fractional-Order Model and Dual Fractional-Order Kalman Filter

Accurate state-of-charge (SOC) estimation of batteries is of great significance for electric vehicles. Ambient temperature influences the relationship between open-circuit voltage (OCV) and SOC as well as model parameter and, accordingly, influences the accuracy of the battery SOC estimation for model-based methods. To address the temperature dependence of battery modeling and SOC estimation, an SOC estimation method for lithium-ion batteries based on a temperature-based fractional first-order RC circuit model and dual fractional-order Kalman filter (DFOKF) was proposed. The OCV-SOC look-up table corresponding to ambient temperature and an offset function regarding to ambient temperature were applied to the developed model for improving modeling accuracy. One of dual filters was used to estimate the SOC, and the other was employed to update the model parameters online for addressing the temperature dependence of model parameter. Comparisons of the SOC estimation results between the developed model and the original model that ignores the influence of ambient temperature under the US06 Highway Driving Schedule and the Federal Urban Driving Schedule (FUDS) tests at eight specified temperatures were performed. The results show that the developed model combined with the DFOKF algorithm improves the accuracy of SOC estimation. For the application of SOC estimation at untested temperature, we proposed to construct the corresponding OCV-SOC look-up table by linear interpolation of the measured OCV-SOC look-up table at the tested temperature. Comparisons of results between using the two kinds of look-up tables to estimate the SOC demonstrate the effectiveness of the proposed approach.

A Study on Highway Driving Assist Evaluation Method Using the Theoretical Formula and Dual Cameras

In this paper, we propose a method to evaluate Highway Driving Assist (HDA) systems using the theoretical formula and dual cameras, which eliminates the need of experts or expensive equipment and reduces the time, effort, and cost required in such tests. A theoretical evaluation formula that can be calculated was proposed and used. The optimal position of the dual cameras, image and focal length correction, and lane detection methods proposed in previous studies were used, and a theoretical equation for calculating the distance from the front wheel of the vehicle to the driving lane was proposed. For the actual vehicle testing, HDA safety evaluation scenarios proposed in previous studies were used. According to the test results, the maximum errors were within 10%. It was determined that the representative cause of the maximum error occurred in the dual camera installed in the test vehicle. Problems such as road surface vibration, shaking due to air resistance, changes in ambient brightness, and the process of focusing the video occurred during driving. In the future, it is judged that it will be necessary to verify the complex transportation environment during morning and evening rush hour, and it is believed that tests will be needed in bad weather such as snow and rain.

Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter

The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability.