Vehicle Collision Avoidance System Research Articles

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles. Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions. However, they suffer from over-conservatism, potentially resulting in false–positive risk events in complicated real-world applications. In this paper, we combine two reachability analysis techniques, a backward reachable set (BRS) and a stochastic forward reachable set (FRS), and propose an integrated probabilistic collision–detection framework for highway driving. Within this framework, we can first use a BRS to formally check whether a two-vehicle interaction is safe; otherwise, a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step. Thus, the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events. To construct the stochastic FRS, we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy. Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data. The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios. The proposed risk assessment framework is promising for real-world applications.

A Novel Prediction Algorithm of Pedestrian Activity Region for Intelligent Vehicle Collision Avoidance System

With the development of intelligent vehicle technology, pedestrian trajectory prediction techniques play a key role in collision avoidance systems. Activity regions are commonly adopted to cover the uncertainty of predicted trajectories. However, setting a rational region size for each pedestrian is a challenging issue. Excessive region size may lead to over-reactions of vehicles and limit the degree of freedom of the path planning while insufficient region size may fail to cover the uncertainty. This work proposes a novel dynamic activity region (DAR) prediction algorithm and a relevant collision risk evaluation method. In the prediction module, the proposed method predicts a rational activity region for each pedestrian based on the historical trajectory in contrast to the conventional methods which adopt preset static activity regions (SAR) for all pedestrians. The comparative study shows that the proposed method reduces the sizes of pedestrian activity regions by 10.5% on average with similar precision. In the cases where the trajectory is highly nonlinear, the region size reduction can reach 12.1%. In the collision risk evaluation module, the risk statuses of pedestrians are classified based on the relationship between their DARs and the future trajectory of the vehicle. This method can produce a more detailed classification. The effectiveness and improvement of the proposed method are validated by experiments and simulations.

Research on Active Collision Avoidance and Hysteresis Reduction of Intelligent Vehicle Based on Multi-Agent Coordinated Control System

This paper provides a multi-agent coordinated control system to improve the real-time performance of intelligent vehicle active collision avoidance. At first, the functions and characteristics of longitudinal and lateral collision avoidance agents are analyzed, which are the main components of the multi-agent. Then, a coordinated solution mechanism of an intelligent vehicle collision avoidance system is established based on hierarchical control and blackboard model methods to provide a reasonable way to avoid collision in complex situations. The multi-agent coordinated control system can handle the conflict between the decisions of different agents according to the rules. Comparing with existing control strategies, the proposed system can realize multi decisions and planning at the same time; thus, it will reduce the operation time lag during active collision avoidance. Additionally, fuzzy sliding mode control theory is introduced to guarantee accurate path tracking in lateral collision avoidance. Finally, co-simulation of Carsim and Simulink are taken, and the results show that the real-time behavior of intelligent vehicle collision avoidance can be improved by 25% through the system proposed.

Collision Avoidance Path Planning for Vehicles Combining MPC and CACC Controllers

The vehicle collision avoidance system has problems such as poor emergency collision avoidance ability and susceptibility to noise interference. Therefore, this study adopts a vehicle tracking model that integrates dynamic constraints and a two car following model to construct a Collaborative Adaptive Cruise Control (CACC) model that integrates Multi Constraints Model Predictive Control (MPC). And conducted simulation experimental research. The results show that in emergency braking collision avoidance conditions, the collision avoidance model designed in this study can quickly respond to the distance control requirements, and the control stability is higher than that of common collision avoidance systems. The standard deviation of the rear car acceleration of the MPC+CACC, reinforcement learning (RL), and proportional-integral-differential (PID) models are 0.18m/s2, 0.25m/s2, and 0.46m/s2, respectively. The car spacing error of the MPC+CACC, RL, and PID models converge to 0m when the time is about 13 seconds, 33 seconds, and 40 seconds, respectively. This study has certain reference significance for improving the collision avoidance ability of China’s intelligent vehicle collision avoidance system.

Implementation of Fatal Vehicle Collision Avoidance System for the Deterrence of Mishaps in Potential Zones

Implementation of Fatal Vehicle Collision Avoidance System for the Deterrence of Mishaps in Potential Zones

Path-following and LiDAR-based obstacle avoidance via NMPC for an autonomous surface vehicle

This paper presents a path-following and collision avoidance system for autonomous surface vehicles based on nonlinear model predictive control. The proposed strategy is capable of following a desired path while maintaining a commanded velocity, whereas it can diverge from the references to safely perform maneuvers to elude unexpected obstacles. A nonlinear dynamic model of the vehicle is applied to predict the vehicle states into a finite horizon. Furthermore, a LiDAR sensor located at the front of the boat is employed for local object detection. Here, the high-performance optimal control framework, acados, is used to solve the optimization problem onboard. Real-time numerical simulations and field experiments demonstrate the effectiveness of the proposed approach against multiple buoys.

Probabilistic Prediction of Collisions between Cyclists and Vehicles Based on Uncertainty of Cyclists’ Movements

The uncertainty of cyclists’ movements has a significant impact on predicting the risk of collisions between cyclists and vehicles. The purpose of this study was to provide a method for assessing collision risk using probability, taking into account the uncertainty of cyclists’ movements. A cyclist model was first developed using a first-order Markov model. Then, based on Monte Carlo sampling, the distribution characteristics of the minimum distance and the time-to-collision (TTC) between the vehicle and the cyclist were extracted. By fitting these features, the probability density functions of the collision distance and TTC were estimated to derive the collision probabilities. The effectiveness of the collision probability prediction model was benchmarked against a deterministic crash risk prediction model (autonomous emergency braking [AEB] system) applied to three real-world cases previously reconstructed in an in-depth crash database. The results show that the collision probability prediction model can effectively predict the risk of collisions between cyclists and vehicles with better accuracy than AEB systems using a fixed trigger threshold. This study is a valuable reference for the development of advanced vehicle collision avoidance systems to protect cyclists and other vulnerable road users.

Developing an improved automatic preventive braking system based on safety-critical car-following events from naturalistic driving study data

In public road tests of autonomous vehicles in California, rear-end crashes have been the most common type of crash. Collision avoidance systems, such as autonomous emergency braking (AEB), have provided an effective way for autonomous vehicles to avoid collisions with the lead vehicle, but to avert false alarms, AEB tends to apply late and hard brake only if a collision becomes unavoidable. Automatic preventive braking (APB) is a new collision avoidance method used in Mobileye’s Responsibility-Sensitive Safety (RSS) model that aims to reduce crashes with a milder brake and decreased impact on traffic flow, but APB’s safety performance is inferior to that of AEB. This study therefore proposes three safety improvement strategies for APB, the addition of response time, safety buffer, and minimum following distance; and combines them in different ways into four improved APB systems, IP1-IP4. Simulating car-following safety–critical events (SCEs) extracted from the Shanghai Naturalistic Driving Study in MATLAB’s Simulink, the safety performance, conservativeness, and driving comfort of the four systems were evaluated and compared with the original APB system, two AEB systems, and human drivers. The results show that 1) IP4, the system that integrated all three strategies, outperformed the baseline APB and IP1-IP3 and prevented all SCEs from becoming crashes; 2) IP4 was slightly more conservative than AEB, but less conservative than RSS; 3) APB’s jerk-bounded braking profile improved driving comfort; and 4) higher deceleration was found in the two AEB systems (both 8.1 m/s2) than in IP4 (6.7 m/s2), but they failed to prevent all crashes. Our proposed APB system, IP4, can provide safe, efficient, and comfortable braking for AVs in car-following SCEs, and has the potential to be practically applied in vehicle collision avoidance systems.

A Looming Spatial Localization Neural Network Inspired by MLG1 Neurons in the Crab Neohelice.

Similar to most visual animals, the crab Neohelice granulata relies predominantly on visual information to escape from predators, to track prey and for selecting mates. It, therefore, needs specialized neurons to process visual information and determine the spatial location of looming objects. In the crab Neohelice granulata, the Monostratified Lobula Giant type1 (MLG1) neurons have been found to manifest looming sensitivity with finely tuned capabilities of encoding spatial location information. MLG1s neuronal ensemble can not only perceive the location of a looming stimulus, but are also thought to be able to influence the direction of movement continuously, for example, escaping from a threatening, looming target in relation to its position. Such specific characteristics make the MLG1s unique compared to normal looming detection neurons in invertebrates which can not localize spatial looming. Modeling the MLG1s ensemble is not only critical for elucidating the mechanisms underlying the functionality of such neural circuits, but also important for developing new autonomous, efficient, directionally reactive collision avoidance systems for robots and vehicles. However, little computational modeling has been done for implementing looming spatial localization analogous to the specific functionality of MLG1s ensemble. To bridge this gap, we propose a model of MLG1s and their pre-synaptic visual neural network to detect the spatial location of looming objects. The model consists of 16 homogeneous sectors arranged in a circular field inspired by the natural arrangement of 16 MLG1s' receptive fields to encode and convey spatial information concerning looming objects with dynamic expanding edges in different locations of the visual field. Responses of the proposed model to systematic real-world visual stimuli match many of the biological characteristics of MLG1 neurons. The systematic experiments demonstrate that our proposed MLG1s model works effectively and robustly to perceive and localize looming information, which could be a promising candidate for intelligent machines interacting within dynamic environments free of collision. This study also sheds light upon a new type of neuromorphic visual sensor strategy that can extract looming objects with locational information in a quick and reliable manner.

Research on Vehicle Collision Avoidance System through using Wireless Vehicle Communication

The number of vehicles in the world has been increasing for many years. With the serious aging of the world, the aging of drivers has become norm. Traffic safety is particularly important. The vehicle safety system has also been significantly improved, such as active braking, lane departure warning, 360-degree panoramic images, etc. All of which have greatly improved the safe driving of vehicle. On the other hand, Lack of the concentration of a driver and degeneration of the reaction time of driver operations are also the main causes of some traffic accidents. Especially, when a vehicle turns at a intersection, a driver must have high judgement. The purpose of this study is to develop the vehicle collision avoidance system through using wireless vehicle communication(VCAS). This system is constructed by a fixed point sensor set up by a road and vehicle information system. A fixed point sensor inform the traffic information to a vehicle. When the vehicle is dangerous condition, VCAS controls the vehicle and avoid a collision. In this paper we would like to examine the application in right turn collision avoidance at intersection.

In-Flight Tests of Intruder Detection Vision System.

In the near future, the integration of manned and unmanned aerial vehicles into the common airspace will proceed. The changes taking place mean that the safety of light aircraft, ultralight aircraft and unmanned air vehicles (UAV) will become an increasing problem. The IDAAS project (Intruder Detection And collision Avoidance System) meets the new challenges as it aims to produce technically advanced detection and collision avoidance systems for light and unmanned aerial vehicles. The work discusses selected elements of research and practical tests of the intruder detection vision system, which is part the of IDAAS project. At the outset, the current formal requirements related to the necessity of installing anticollision systems on aircraft are presented. The concept of the IDAAS system and the structure of algorithms related to image processing are also discussed. The main part of the work presents the methodology developed for the needs of dedicated flight tests, its implementation and the results obtained. The initial tests of the IDAAS system carried out on an ultralight aircraft generally indicate the possibility of the effective detection of intruders in the airspace with the use of vision methods, although they also indicated the existence of conditions in which this detection may prove difficult or even impossible.

Collision avoidance control for Unmanned Autonomous Vehicles (UAV): Recent advancements and future prospects

The recent advances in collision avoidance technologies for unmanned vehicles such as UAVs, AUVs, AGVs, and USVs have greatly advanced the industry. Their lower cost and acceptability of high-risk missions have enabled the development of collision avoidance controllers for autonomous vehicles. These low-maintenance gadgets are also portable, need low maintenance, and enable continuous monitoring to occur near real-time. This may be said; however it would be incorrect, because collision avoidance controllers have been related with compromises that affect data dependability. Research on collision avoidance controls is quickly developing; therefore it is distributed throughout multiple papers, projects, and grey literature. This report critically reviews the recent relevant research on creating collision avoidance systems for autonomous vehicles. Typically, the assessment measures are dependent on the algorithm's use case and the platform's capabilities. The full evaluation of the benefits and drawbacks of the most prevalent approaches in the present state of the art is provided based on 7 metrics which are complexity, communication dependence, pre-mission planning, robustness, 3D compatibility, real-time performance and escape trajectories.

A study of multiple reward function performances for vehicle collision avoidance systems applying the DQN algorithm in reinforcement learning

Reinforcement Learning (RL) is an area of Machine Learning (ML) that intends to improve the acts of agents learning from environmental interconnection. The significant concern in RL is to achieve the promising potential of the training process in the model. However, network convergence speed is often sluggish in RL and converges quickly to local optimal solutions. Reward function has been used to deal with these problems as a useful tool to speed up the agent’s learning speed. Even though RL convergence properties have been comprehensively explored, there are no specific rules for choosing the reward function. Therefore, searching for efficient potential reward function is still an exciting field of study. This paper discusses the reward function, execute some analysis, and provides the learning agent with the extracted information to increase the speed of learning for collision avoidance task. We provide an experimental study for selecting one reward function in a simulated collision-avoidance environment of an autonomous vehicle by applying the DQN algorithm. It has been conducted on online environments, which is using the CARLA simulator. This experimental study consists of three cases with a various exploration of reward values. Case 1 consists of the range of the penalty value larger than the reward function by 200 times. Case 2 is similar, but with the small range number of the penalty is applied, case 3, which is the reward function and penalty value, is in the same range value. The result shows that case 3 performances outperform case 1 and case 2 with 94% average accuracy; meanwhile, case 1 obtains 70%, and case 2 achieves 85% accuracy. It is may due to the monumental size of the collision penalty in comparison to all else. Hence, the findings obtained show the efficacy of the exploration of the reward function.

Autonomous Collision Avoidance Using MPC with LQR-Based Weight Transformation.

Model predictive control (MPC) is a multi-objective control technique that can handle system constraints. However, the performance of an MPC controller highly relies on a proper prioritization weight for each objective, which highlights the need for a precise weight tuning technique. In this paper, we propose an analytical tuning technique by matching the MPC controller performance with the performance of a linear quadratic regulator (LQR) controller. The proposed methodology derives the transformation of a LQR weighting matrix with a fixed weighting factor using a discrete algebraic Riccati equation (DARE) and designs an MPC controller using the idea of a discrete time linear quadratic tracking problem (LQT) in the presence of constraints. The proposed methodology ensures optimal performance between unconstrained MPC and LQR controllers and provides a sub-optimal solution while the constraints are active during transient operations. The resulting MPC behaves as the discrete time LQR by selecting an appropriate weighting matrix in the MPC control problem and ensures the asymptotic stability of the system. In this paper, the effectiveness of the proposed technique is investigated in the application of a novel vehicle collision avoidance system that is designed in the form of linear inequality constraints within MPC. The simulation results confirm the potency of the proposed MPC control technique in performing a safe, feasible and collision-free path while respecting the inputs, states and collision avoidance constraints.

Vehicle Speed Control and Accident Prevention System in Smog Zone

The air quality index (AQI) is a proportion of what air contamination means for one’s wellbeing inside a brief timeframe. The higher the AQI esteem, the more noteworthy the degree of air contamination. In a smog zone, the air pollutants can be detected with an air quality detector that will provide information about invisible toxic pollutant matters present in the air. This paper is concentrated on vehicle collision avoidance in smog places. For instance in the road, the driver suffers from unclear vision because PM (Particulate Matter) enters his/her eyes which leads to vehicle collision. A vehicle collision avoidance system with a microcontroller and onboard sensors in the smog zone is developed. It mainly focuses on the digital driver-vehicle interface implementation by using CAN protocol. The framework of CAN lessens wiring by and large. In this system, information like humidity, temperature, and distance is obtained. The humidity module measures both the temperature and humidity content and an the ultrasonic sensor is adjusted to gauge the distance between the object and vehicle, if a hindrance is identified inside 300cm from the vehicle, the alarm gives a signal to the driver. Once an individual vehicle enters the smog zone, it lowers the speed.

Design of an Acceleration Redistribution Cooperative Strategy for Collision Avoidance System Based on Dynamic Weighted Multi-Objective Model Predictive Controller

Road traffic accidents, especially those accidents with multiple-vehicle collision usually cause injuries and mortalities. Currently, studies on collision avoidance mainly focus on the control strategies for adjacent two vehicles or multiple vehicles in a single platoon direction. This paper proposes a bi-directional collision avoidance system for multiple vehicles to minimize the collision risk under the model predictive control (MPC) framework through switching the vehicle-following mode based on the inter-vehicular states. A hierarchical structure with an upper layer and a lower layer is designed. A dual-operational mode switching strategy and the vehicle-following model are formulated in the upper layer, together with the development of the acceleration redistribution cooperative strategy for vehicle platoon. While the lower layer is designed to track the desired acceleration received from the upper layer by considering the practical situation of the control commands. To tackle complex transitional operation, a dynamic weighted tuning strategy is proposed and integrated it with the MPC. The numerical results show that the proposed system outperforms the conventional collision avoidance system and is effective to avoid a collision or minimize the total impact of the vehicle platoon when the collision is unavoidable.

The method of forming virtual receiving channels in the automobile MIMO-radar

The article is intended for specialists in the field of radar, radio engineering and telecommunications. It considers the problem of forming virtual receiving channels in a car MIMO radar to increase the antenna aperture in the horizontal plane. The case is investigated when two cars fall into the main beam of the radar antenna pattern, and therefore, the application of super-resolution methods in azimuth is required. In modern vehicles, this option is required for vehicle collision avoidance and driver assistance systems. The use of MIMO technology makes it possible to form a larger antenna array (AR) for reception, the so-called virtual AR. This becomes possible due to a special choice of the topologies of the location of the transmitting and receiving channels, as well as due to the multiplication of the probing signals in each transmitting channel by an individual code. As a result, the resolving power of the AR increases in the direction finding of the target. A radar with “short” sounding chirp pulses is considered, in which the range is measured by the frequency method. The parameters of the signal in the radar are chosen so that the contribution of the terms associated with the speed of the target on the pulse duration to the beat frequency on the receiving side is negligible. Analytical expressions are obtained for the received signals under conditions of the Doppler frequency shift and “short” probing pulses. A method for generating signals in virtual receiving channels using an encoder only on the transmitting side and without a decoder on the receiving side is proposed. The use of various codes is investigated and their effectiveness is compared. For direction finding of targets, the method of the minimum polynomial of the correlation matrix of the received signals is applied. The method involves assessing the degree of the minimum polynomial of the correlation matrix of the input process in the AR based on a statistically valid root-mean-square criterion. This method allows adaptive estimation of the number of signal sources and has a super-resolution function. In contrast to the known works, the main attention is paid to the case of a short sample of the input process, when the number of samples is less than the dimension of the virtual AR. In this case, the sample correlation matrix is degenerate. The results of numerical modeling are given for the accuracy of azimuth measurement and the probability of correct resolution of two targets. A natural experiment was carried out. The presented results demonstrate the efficiency of the proposed concept, high accuracy of azimuth measurements, and the possibility of super resolution of two targets in the case of a short sample.

Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi‐speed scheduler

This study presents an adaptive motion planning strategy for automated vehicle collision avoidance systems to be associated with the variation of collision speed region based on the position of the obstacle. This is done by designing the motion planner using an artificial potential field (APF) with the incorporation of an adaptive multi-speed scheduler using fuzzy system in the motion planning structure. The knowledge database information is developed based on the risk perception of the driver that consists of APF parameters and was optimised by using particle swarm optimisation algorithm. This study contributes to the improvement of a feasible reference motion generated by the motion planner that can be converted into desired control signals. The reference motion resulted to provide the control command that managed to avoid collision successfully by evasive manoeuvre without lane departure when adapting to variation in the vehicle speeds with different obstacle positions. The results indicated the reduction of the lateral error with respect to the reference avoidance trajectory data of up to 87% compared to base-type APF with maximum reference lateral motion is reduced of up to 26%. Then, a hardware-in-loop test is conducted to verify the proposed strategy using a steering wheel system.

Safety distance model for longitudinal collision avoidance of logistics vehicles considering slope and road adhesion coefficient

With the acceleration of urbanization process, the country’s strong support for the healthy development of the logistics industry has made urban logistics become a hot topic in recent years. With the increase in the number of logistics vehicles, traffic accidents have become more frequent. Intelligent vehicle collision avoidance system is an important part of advanced safety technology. To increase veracity and practicability of logistics vehicle safety collision avoidance, this paper presents a safety distance model for longitudinal collision avoidance of logistics vehicles considering road slope and road adhesion coefficient. Based on the vehicle kinetic theory, the information of surrounding environment for the vehicle is obtained using environment sensing system adequately, a method is designed to estimate the road slope and road adhesion coefficient. Combined with vehicle dynamics and tire normal force variation, the road slope was estimated. Based on the relationship between slip rate and adhesion coefficient, the Least Square Method is used for multivariate fitting to obtain the relationship between rolling resistance coefficient and road adhesion coefficient, estimate the road adhesion coefficient, and the maximum deceleration of vehicle braking is modified. Therefore, the safety distance model is established. In order to verify the accuracy and effectiveness of the model, three cases are designed for verification: case I is verification of the safety distance model considering the slope factor; case II is verification of the safety distance model considering the road adhesion factor; case III is the safety distance verification considering both the factors of slope and road adhesion coefficient. The result shows that it is necessary to take the factors of road slope and adhesion coefficient into the safety distance model to improve the accuracy of the model.

Hybrid Collision Avoidance for ASVs Compliant With COLREGs Rules 8 and 13-17.

This paper presents a three-layered hybrid collision avoidance (COLAV) system for autonomous surface vehicles, compliant with rules 8 and 13–17 of the International Regulations for Preventing Collisions at Sea (COLREGs). The COLAV system consists of a high-level planner producing an energy-optimized trajectory, a model-predictive-control-based mid-level COLAV algorithm considering moving obstacles and the COLREGs, and the branching-course model predictive control algorithm for short-term COLAV handling emergency situations in accordance with the COLREGs. Previously developed algorithms by the authors are used for the high-level planner and short-term COLAV, while we in this paper further develop the mid-level algorithm to make it comply with COLREGs rules 13–17. This includes developing a state machine for classifying obstacle vessels using a combination of the geometrical situation, the distance and time to the closest point of approach (CPA) and a new CPA-like measure. The performance of the hybrid COLAV system is tested through numerical simulations for three scenarios representing a range of different challenges, including multi-obstacle situations with multiple simultaneously active COLREGs rules, and also obstacles ignoring the COLREGs. The COLAV system avoids collision in all the scenarios, and follows the energy-optimized trajectory when the obstacles do not interfere with it.