Ego Vehicle Research Articles

Lateral Driver‐Automation Driver Authority Decision Considering Safety of the Intended Functionality

AbstractThe driver authority decision for driver‐initiated takeover is closely related to vehicle driving safety. In this paper, a lateral driver‐automation driver authority decision method considering the safety of the intended functionality is proposed to improve vehicle driving safety. Based on systems‐theoretic processes analysis, functional safety analysis of driver initiative takeover is carried out to clarify the safety of the intended functionality issues caused by the unreasonable decision. Then, a method for defining the safe driving area is developed to assess the safety level of the driving area around the ego vehicle. Taking the driver‐vehicle state parameters and vehicle–vehicle state parameters as input, a deep neural network model is constructed to determine the intention of the driver to take over the vehicle. The lateral driver‐automation driver authority decision method is designed to make the driver‐machine take‐over decisions, which considers the safety of the intended functionality. The effectiveness of the proposed method is evaluated via numerical simulation and hardware‐in‐the‐loop experiments. The results show that the designed method not only improves the control ability of the driver to the maximum extent but also integrates the perception ability of the driver into the vehicle control system to improve further vehicle driving safety.

A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy

Convoys composed of autonomous vehicles could improve the transportation and freight industries in several ways. One of the avenues of improvement is in fuel efficiency, where the vehicles maintain a close following distance to each other in order to reduce air resistance by way of the draft effect. While close following distances improve fuel efficiency, they also reduce both the margin of safety and the system’s tolerance to disturbances in relative position. The system’s tolerance to disturbances is known as string stability, where the error magnitude either grows or decays as it propagates rearward through the convoy. One of the major factors in a system’s string stability is its delay in sending state updates to other vehicles, the most pertinent being a hard braking maneuver. Both external sensors and vehicle-to-vehicle communication standards have relatively long delays between peer vehicle state changes and the information being actionable by the ego vehicle. The system presented here, called the Convoy Vehicular Ad Hoc Network (Convoy VANET), was designed to reliably propagate emergency event messages with low delay while maintaining reasonable channel efficiency. It accomplishes this using a combination of several techniques, notably relative position-based retransmission delays. Our results using Network Simulator 3 (ns3) show the system propagating messages down a 20-vehicle convoy in less than 100 ms even with more than a 35% message loss between vehicles that are not immediately adjacent. These simulation results show the potential for this kind of system in situations where emergency information must be disseminated quickly in low-reliability wireless environments.

Designing a Real-Time Implementable Optimal Adaptive Cruise Control for Improving Battery Health and Energy Consumption in EVs through V2V Communication

Battery electric vehicles (BEVs) face challenges like their limited all-electric range, the discrepancy between promised and actual energy efficiency, and battery health degradation, despite their environmental benefits. This article proposes an optimal adaptive cruise control (OACC) framework by leveraging ideal vehicle-to-vehicle communication to address these challenges. In a connected vehicle environment, where it is assumed that the Ego vehicle’s vehicle control unit (VCU) accurately knows the speed and position of the Leading vehicle, the VCU can optimally plan the acceleration trajectory for a short-term future time window through a model predictive control (MPC) framework tailored to BEVs. The primary objective of the OACC is to reduce the energy consumption and battery state-of-health degradation of a BEV. The Chevrolet Spark 2015 is chosen as the BEV platform used to validate the effectiveness of the proposed OACC. Simulations conducted under urban and highway driving conditions, as well as under communication delay and infused noise, resulted in up to a 3.7% reduction in energy consumption and a 9.7% reduction in battery state-of-health (SOH) degradation, demonstrating the effectiveness and robustness of the proposed OACC.

Co-simulation of giant magnetostrictive autonomous emergen-cy braking system for intelligent vehicles based on Prescan/Simulink /CarSim

The combination of giant magnetostrictive actuator (GMA) and autonomous Emergency brak-ing system (AEB) can achieve the lightweight body and fast braking of intelligent vehicle. In this essay, simulation scenarios are established in PreScan, AEB algorithm and GMA actuator are combined in simulink, vehicle dynamics model is provided by CarSim, and co-simulation analysis of CCRm, CCRs and CCRb working conditions is carried out. When AEB adopts TTC algorithm only, in the stationary vehicle scenario (CCRs) where the vehicle approaches the front at a certain speed, the relative distance between the two vehicles is between 1.86 and 2.85m until stopping. When the front vehicle is driving slowly and the ego vehicle is faster than the front (CCRm), the relative distance between the two vehicles until stopping is between 2.28 and 2.83m; When the ego vehicle is driving at the same speed as the front, the front vehicle suddenly braking (CCRb), and the relative distance between the two vehicles until stopping is 1.98~2.54m; The above scenarios can achieve effective collision avoidance, and the safety distance is within an effective and reasonable range. In the scenario of two vehicles following at the same speed and close distance, TTC algorithm fails. AEB adopts TTC algorithm and Seungwukmoonalgo-rithm for collaborative control to achieve effective collision avoidance. The co-simulation re-sults show that the AEB system can intervene in time and avoid collisions effectively under the above three working conditions, and the relative distance between the two vehicles until stop-ping is between 1.86 and 2.85m, which verifies the timeness and effectiveness of the braking of the giant magnetostrictive actuator when AEB algorithm intervenes, and significantly improves the active safety performance of the vehicle.

Learning Cross-modality Interaction for Robust Depth Perception of Autonomous Driving

As one of the fundamental tasks of autonomous driving, depth perception aims to perceive physical objects in three dimensions and to judge their distances away from the ego vehicle. Although great efforts have been made for depth perception, LiDAR-based and camera-based solutions have limitations with low accuracy and poor robustness for noise input. With the integration of monocular cameras and LiDAR sensors in autonomous vehicles, in this article, we introduce a two-stream architecture to learn the modality interaction representation under the guidance of an image reconstruction task to compensate for the deficiencies of each modality in a parallel manner. Specifically, in the two-stream architecture, the multi-scale cross-modality interactions are preserved via a cascading interaction network under the guidance of the reconstruction task. Next, the shared representation of modality interaction is integrated to infer the dense depth map due to the complementarity and heterogeneity of the two modalities. We evaluated the proposed solution on the KITTI dataset and CALAR synthetic dataset. Our experimental results show that learning the coupled interaction of modalities under the guidance of an auxiliary task can lead to significant performance improvements. Furthermore, our approach is competitive against the state-of-the-art models and robust against the noisy input. The source code is available at https://github.com/tonyFengye/Code/tree/master .

PRORETA 5 – building blocks for automated urban driving enhancing city road safety

Abstract In the joint research project PRORETA 5, building blocks for automated driving in urban areas have been developed, implemented, and tested. The developed blocks involve an object tracking for cars, bicycles, and pedestrians that feeds a multimodal object prediction which is able to predict the traffic participants’ most likely trajectories. Then, an anytime tree-based planning algorithm calculates the vehicle’s desired path. Finally, logic-based safety functions ensure a collision-free trajectory for the ego vehicle. The mentioned building blocks were integrated and tested in a prototype vehicle in urban scenarios. Furthermore, a novel general framework for specifying and testing traffic rule compliance has been developed. In this paper, the automated driving concept of PRORETA 5 is introduced and the developed methods are briefly explained.

Occlusion-aware collision avoidance trajectory planning with potential collision risk assessment for autonomous vehicle

Motion planning for autonomous vehicles relies heavily on perception and prediction results to find a safe, collision-free local trajectory that adheres to traffic rules. However, vehicle perception is frequently limited by occlusion, and the generation of safe local trajectories with restricted perception is a significant challenge in the field of motion planning. This paper introduces a collision avoidance trajectory planning algorithm that considers potential collision risks, within a hierarchical framework of sampling and optimization. The primary objective of this work is to generate trajectories that are safer and align better with human driver behavior while considering potential collision risks in occluded regions. Specifically, in occlusion scenarios, the state space is discretized, and a dynamic programming algorithm is used for a sampling-based search to obtain initial trajectories. Additionally, the concept of a driving risk field is introduced to describe potential collision risk elements within the human-vehicle-road environment. By drawing inspiration from graph search algorithms, potential collision risk areas are accurately described, and a cost function is proposed for evaluating potential risks in occluded regions. Drivers typically exhibit conservative and cautious driving behavior when navigating through occluded regions. The proposed algorithm not only prioritizes driving safety but also considers driving efficiency, thereby reducing the vehicle's conservativeness when passing through occlusions. The research results demonstrate that the ego vehicle can actively avoidblind spots and tends to move away from occluded regions, aligning more closely with human driver behavior.

A dynamic driving-style analysis method based on drivers’ interaction with surrounding vehicles

The ability to recognize different driving styles of surrounding vehicles is crucial to determine the safest and most efficient driving decisions, prevent accidents, and analyze the causes of traffic accidents. Understanding if the surrounding vehicle is aggressive or cautious can greatly assist in the decision making of vehicles in terms of whether and when it is appropriate to make particular maneuvers. A driver’s driving style usually changes with the environment, which brings great challenges to the current research. To this end, a dynamic driving-style analysis framework, in which drivers’ interactions with other vehicles are considered, is proposed in this article. First, by analyzing common traffic scenarios, five surrounding vehicles are selected as the environmental vehicles to be considered. Time headway (THW) and time to collision (TTC) that can consider the relative speed and position with the ego vehicle are selected as the clustering indicators. Then, a Bayesian nonparametric learning method based on a hierarchical Dirichlet-process hidden semi-Markov model (HDP-HSMM) is introduced to extract primitive driving patterns from time-series driving data without prior knowledge of the number of these patterns. Then the driving pattern is scored according to the risk degree. A driver’s aggressiveness is scored and drivers are divided into different styles based on the frequency distribution of driving patterns. The effectiveness of the proposed method is demonstrated on a real-world vehicle trajectory data set where results show that driving pattern switches and more complex driving behaviors can be better captured and understood semantically.

A quantitative blind area risks assessment method for safe driving assistance

The task of driving in complex and occluded environments presents significant challenges, as it requires the perception and prediction of one’s surrounding environment. This is especially important as blind spots, or areas that are difficult to directly detect due to occlusion by surrounding vehicles or obstacles, which can pose a serious risk of accidents. To address this issue, this study proposes a method called “Uncertainty Shadow Safety”, which aims to evaluate the potential risk of invisible areas surrounding the ego vehicle based on the combination of static road information and the presence of traffic participants. The proposed system calculates the “shadow uncertainty” of a non-visible road area based on its physical properties and the probabilities of various traffic participants being present in that area. It then performs a quantitative assessment of the potential risk from various directions, taking into account the surrounding anomalous movement states. Simulation tests were conducted in various scenarios to demonstrate the feasibility and effectiveness of the Uncertainty Shadow Safety system. The advanced warning provided by the system to potential risks in invisible areas can help to reduce the likelihood of collisions and improve driving safety for both human drivers and autonomous vehicles.

Knowledge Distillation from Single-Task Teachers to Multi-Task Student for End-to-End Autonomous Driving

In the domain of end-to-end autonomous driving, conventional sensor fusion techniques exhibit inadequacies, particularly when facing challenging scenarios with numerous dynamic agents. Imitation learning hampers the performance by the expert and encounters issues with out-of-distribution challenges. To overcome these limitations, we propose a transformer-based algorithm designed to fuse diverse representations from RGB-D cameras through knowledge distillation. This approach leverages insights from multi-task teachers to enhance the learning capabilities of single-task students, particularly in a Reinforcement Learning (RL) setting. Our model consists of two primary modules: the perception module, responsible for encoding observation data acquired from RGB-D cameras and performing tasks such as semantic segmentation, semantic depth cloud mapping (SDC), ego vehicle speed estimation, and traffic light state recognition. Subsequently, the control module decodes these features, incorporating additional data, including a rough simulator for static and dynamic environments, to anticipate waypoints within a latent feature space. Vehicular controls (e.g., steering, throttle, and brake) are obtained directly from measurement features and environmental states using the RL agent and are further refined by a PID algorithm that dynamically follows waypoints. The model undergoes rigorous evaluation and comparative analysis on the CARLA simulator across various scenarios, encompassing normal to adversarial conditions. Our code is available at https://github.com/pagand/e2etransfuser/ to facilitate future studies.

Proactive Braking Control System for Collision Avoidance during Right Turns with Occluded Vision at an Intersection

This paper describes the development of an Advanced Driver Assistance System (ADAS) which will allow drivers to avoid collisions with an oncoming vehicle from an occluded area when turning right at intersections in left-hand traffic. Connected vehicles, in coordination with infrastructure, represent one of the commercialized ADAS technologies for collision avoidance. However, the coverage of the ADAS will be limited to designated intersections only, as communication equipment needs to be installed in both the vehicle and infrastructure to enable the assistance. This paper proposes an ADAS using on-board sensors, independent of infrastructure facilities, to control the vehicle velocity to avoid collisions. Most current intersection assistance, by using an Autonomous Emergency Braking System (AEBS), allows the driver to avoid a collision with oncoming vehicles when there is clear vision without occlusion. However, many accidents occur when the vehicle detects the oncoming vehicle too late because of occlusion in the intersection, such as a vehicle in the opposite lane. This system calculates the hazardous speed criteria of the ego vehicle, which might result in a high risk of collision when darting out occurs, and provides speed control assistance to allow the driver to escape from the hazardous speed region. The simulation results reveal that the proposed system effectively reduces the possibility of collisions compared to conventional AEBS.

Expressway lane change strategy of autonomous driving based on prior knowledge and data-driven

Automatic driving in expressways is generally considered to be the easiest for commercial landing, and vehicle behavior decision-making is the core of automatic driving technology, which directly affects the safety and comfort of vehicle driving. In this paper, an automatic lane change strategy based on prior knowledge and data-driven is proposed for expressway. Our method decouples autonomous driving decisions into two processes. Firstly, we propose an optimization model of lane change features based on prior knowledge, which not only provides a basis for the selection of vehicle features but also effectively reduces the interference of invalid features. The driving risk field is used to screen the vehicle targets that have potential influence on the lane change of the ego vehicle, and the maximal information coefficient evaluates the effectiveness of the features of the vehicle targets through the maximal information coefficient to optimize the lane change features. Then, a data-driven lane-changing model is proposed based on Attention-BiLSTM (Bi-directional Long Short-Term Memory), which transforms vehicle lane-changing into time series prediction, fully explores the relationship between lane-changing features in the context information, and effectively improves the anti-interference ability of the system against accidental errors. In addition, the attention mechanism can adaptively adjust the weight of lane-changing features and effectively capture the important information that needs attention in different lane-changing states, further improving the accuracy of the model. Finally, the validity of the model is verified by remote sensing data sets and real vehicle experiments. The test results show that the test accuracy of our method is 95.1% in data sets and 94.2% in real vehicle experiments, which fully meets the requirements of lane change decisions about automatic driving.

Deep neural network for lateral control of self-driving cars in urban environment

<span>The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control. </span>

Distributed MPC for Multi-Vehicle Cooperative Control Considering the Surrounding Vehicle Personality

In real traffic environment, a single control mode of traditional autonomous vehicles cannot meet various driving requirements for different drivers, which will decrease the acceptance of autonomous vehicles, and even may further cause traffic risks. This paper studies the cooperative strategies between ego vehicle and surrounding vehicles with the naturalistic experiment data, and then designs an autonomous vehicle control method based on the distributed Model Predictive Control (MPC) in order to consider the interaction relationship of ego vehicles and surrounding vehicles. Finally, the proposed method is verified by software simulation and Hardware in the Loop (HIL) simulation experiments, and the experiment results demonstrate that the control method proposed in this paper not only can control the vehicle to complete the typical driving tasks smoothly, in terms of car-following and lane-changing, but also can reflect the different cooperative strategies among different driving behavior characteristics, which can improve safety and acceptance of autonomous vehicles to promote the practical application of autonomous vehicle technology.

Research on Lane-Changing Decision Making and Planning of Autonomous Vehicles Based on GCN and Multi-Segment Polynomial Curve Optimization.

This paper considers the interactive effects between the ego vehicle and other vehicles in a dynamic driving environment and proposes an autonomous vehicle lane-changing behavior decision-making and trajectory planning method based on graph convolutional networks (GCNs) and multi-segment polynomial curve optimization. Firstly, hierarchical modeling is applied to the dynamic driving environment, aggregating the dynamic interaction information of driving scenes in the form of graph-structured data. Graph convolutional neural networks are employed to process interaction information and generate ego vehicle's driving behavior decision commands. Subsequently, collision-free drivable areas are constructed based on the dynamic driving scene information. An optimization-based multi-segment polynomial curve trajectory planning method is employed to solve the optimization model, obtaining collision-free motion trajectories satisfying dynamic constraints and efficiently completing the lane-changing behavior of the vehicle. Finally, simulation and on-road vehicle experiments are conducted for the proposed method. The experimental results demonstrate that the proposed method outperforms traditional decision-making and planning methods, exhibiting good robustness, real-time performance, and strong scenario generalization capabilities.

Research on car-following control and energy management strategy of hybrid electric vehicles in connected scene

To address the comprehensive optimization problem of driving performance and fuel economy in the driving process of hybrid electric vehicles (HEV) in the car-following scene in the connected environment, an energy management strategy (EMS) based on front vehicle speed prediction and ego vehicle speed planning is designed by combining intelligent transportation system (ITS) technology. The front vehicle speed predictor is first established based on the long short-term memory neural network (LSTM). Then, based on the predicted speed of the front car, the predictive cruise control (PCC) strategy is designed for realizing the speed control in the car-following scene by combining it with the adaptive cruise control (ACC). Finally, based on the planned vehicle speed, deep reinforcement learning (DRL)-based EMS is used to optimize the power distribution among different power components of HEVs. The analysis of simulation results under the SUMO-Python joint simulation platform verifies the proposed strategy.

Controlling a vehicle braking and longitudinal acceleration using a seeking control approach

Traditional methods for tracking the paths of driverless vehicles use plant models to determine the corresponding control laws. Due to the intricate interactions between the road and the tires, time-varying characteristics, and unidentified disturbances. It is challenging to create an accurate vehicle model. As a result, data-driven controllers, which are independent of a predetermined plant model are becoming more and more well-liked. This work implements adaptive cruise control (ACC) by employing a control approach called extremum seeking technique (EST), which is a model-free control (MFC), to control a vehicle braking and longitudinal acceleration. The main aim here is to create an ego vehicle that travels at a specific speed with maintaining a secure space with respect to a guide vehicle. A car including an ACC technique called ego car, exploits radar to determine relative velocity and relative space relating to the guiding car. The ACC technique is considered to keep maintain a relatively secure space or a preferred cruising velocity concerning the guiding vehicle. The developed model succeeded to determine the relative velocity and relative space according for the ego car to another guiding car with acceleration not more than ±2 m/s2 and spacing error less than 6 m.

Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach

Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach

Safe Motion Planning and Control Framework for Automated Vehicles with Zonotopic TRMPC

Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles (AVs). The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer. To address this issue, this study proposes a safe motion planning and control (SMPAC) framework for AVs. For the control layer, a dynamic model including multi-dimensional uncertainties is established. A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set. A flexible tube with varying cross-sections is constructed to reduce the controller conservatism. For the planning layer, a concept of safety sets, representing the geometric boundaries of the ego vehicle and obstacles under uncertainties, is proposed. The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories. An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles. A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC. The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.

Coordinated Decision Control of Lane-Change and Car-Following for Intelligent Vehicle Based on Time Series Prediction and Deep Reinforcement Learning.

Adaptive cruise control and autonomous lane-change systems represent pivotal advancements in intelligent vehicle technology. To enhance the operational efficiency of intelligent vehicles in combined lane-change and car-following scenarios, we propose a coordinated decision control model based on hierarchical time series prediction and deep reinforcement learning under the influence of multiple surrounding vehicles. Firstly, we analyze the lane-change behavior and establish boundary conditions for safe lane-change, and divide the lane-change trajectory planning problem into longitudinal velocity planning and lateral trajectory planning. LSTM network is introduced to predict the driving states of surrounding vehicles in multi-step time series, combining D3QN algorithm to make decisions on lane-change behavior. Secondly, based on the following state between the ego vehicle and the leader vehicle in the initial lane, as well as the relationship between the initial distance and the expected distance with the leader vehicle in the target lane, with the primary objective of maximizing driving efficiency, longitudinal velocity is planned based on driving conditions recognition. The lateral trajectory and conditions recognition are then planned using the GA-LSTM-BP algorithm. In contrast to conventional adaptive cruise control systems, the DDPG algorithm serves as the lower-level control model for car-following, enabling continuous velocity control. The proposed model is subsequently simulated and validated using the NGSIM dataset and a lane-change scenarios dataset. The results demonstrate that the algorithm facilitates intelligent vehicle lane-change and car-following coordinated control while ensuring safety and stability during lane-changes. Comparative analysis with other decision control models reveals a notable 17.58% increase in driving velocity, underscoring the algorithm's effectiveness in improving driving efficiency.