ABSTRACT Identity‐based fully homomorphic signature (IBFHS) allows untrusted servers to conduct homomorphic evaluations on outsourced data to obtain a new valid signature, ensuring the evaluated output's correctness while significantly simplifying key management. However, the public composability of IBFHS limits its applicability in scenarios requiring restricted verification rights. For example, in cloud storage data audit systems, introducing a designated verifier mechanism ensures that only authorized third‐party auditors (TPAs) can verify data integrity, preventing malicious auditors from tampering with or forging results. To address this limitation, we propose an identity‐based strong designated verifier fully homomorphic signature (IBSDVFHS) scheme, where only an authorized entity can verify the validity of homomorphically evaluated signatures. We establish formal security definitions for IBSDVFHS, including unforgeability, nontransferability, privacy of the signer's identity, and robustness. Furthermore, we propose a specific design of IBSDVFHS with provable security under the small integer solution (SIS) assumption and the learning with errors (LWE) assumption in the random oracle model.

Heterogeneous autonomous vehicle (HAV) coordinated motion planning must guide each vehicle out of the conflict zone based on the differences in vehicle platform characteristics. Decomposing complex driving tasks into primitives is an effective way to improve algorithm efficiency. Hence, the purpose of this paper is to complete the coordinated motion planning tasks through offline driving behavior primitive (DBP) library generation, online extension and selection of DBPs. The proposed algorithm applies dynamic movement primitives and singular value decomposition to learn driving behavior patterns from driving data, integrates them into a model-based optimization generation method as constraints, and builds a DBP library by fusing driving data and vehicle model. Based on the generated DBP library and primitive association probabilities learned from labeled driving segments via stochastic context-free grammar, the planning method completes the independent DBP extension of each vehicle in the conflict zone, generates an interaction DBP tree, and uses the mixed-integer linear programming algorithm to optimally select the primitives to be executed. Fig 1. shows the flowchart of the proposed coordinated motion planning method. We also present how to utilize the DBP libraries to obtain coordinated motion planning results with spatiotemporal information in the form of DBP extension and selection. The results obtained by real vehicle platforms and simulation show that the proposed method can accomplish coordinated motion planning tasks without relying on specific scene elements and highlight the unique motion characteristics of HAVs.

Revealing differences in vehicle characteristics is critical to enhancing the accessibility of heterogeneous vehicles in off-road environments. Decomposing complex motions into primitives facilitates the maintenance of the algorithm’s solution efficiency while considering various constraints. Therefore, this paper proposes a heterogeneous vehicle motion planning method for off-road scenarios based on the generation, extension, and selection of driving behavior primitives. Based on the library of heterogeneous vehicle driving behavior primitives (HDBPs) extracted from driving data in our previous study, this paper proposes a primitive offline optimization generation method that integrates driving behavior constraints, vehicle kinematics constraints, reserved power constraints, and ground adhesion constraints. The generation of spatiotemporal coupled planning results is accomplished by HDBP extension and selection using the optimized HDBP library as the source. In particular, the extension and selection cost considers the interaction with the ground under the constraints of the suspension system, as well as the capacity of the drive system. This paper demonstrates that the proposed HDBP-based planning method can generate highly adaptable and differentiated primitive sequences based on diverse terrain conditions and heterogeneous vehicle characteristic constraints. Moreover, benefiting from the suspension-based pose estimation and drive system characteristic limitations, the method proposed in this paper has a significant advantage over the comparison methods in terms of terrain traversability in real-scene motion planning experiments.

Optimizing the composition of the desired trajectory and creating an appropriate control method are essential to improve the tracking control effect. Decomposition and optimal combination of primitives is a general and practical way of composing desired trajectories. Therefore, the purpose of this paper is to generate control-system-adapted driving behavior primitives (CDBPs) and design the corresponding control strategy. Based on the pre-constructed driving behavior primitive library extracted from driving data, a nonlinear optimization method is applied to optimize the trajectories that do not conform to the vehicle kinematic constraints during the primitive offline generalization process. In addition to providing time-series trajectory points for tracking control, the optimized primitives contain reference control quantities for linearizing the online control system, as well as optimal controller parameters generated based on fuzzy logic with respect to the category of primitives. Moreover, the control optimization problem at the primitive transition segment is solved by introducing a game-coordinated control strategy. Simulation results demonstrate that the CDBP-based control method proposed in this paper can enhance the control accuracy within the primitives and also effectively solve the smooth transition issue between the primitives.

Tracking by detection(TBD) method has achieved great improvements for its high efficiency, extensibility and portability, but it still struggles on computational efficiency. Many recently proposed methods improve performance by integrating appearance similarity and simply extract appearance feature for all the targets. This results redundant calculations as some targets can already be easily tracked without feature extraction, such as targets walking alone. In this work, we tackle the efficiency problem from a new perspective and propose AETrack, an efficient approach for online multi-object tracking(MOT), which integrates three association metrics through a novel cascaded matching strategy. Instead of simply computing all the association metrics for all tracklets, our matching strategy dynamically chooses and fuses the metrics for each tracklet considering both effectiveness and efficiency. Inference speed is boosted greatly and accuracy is still competitive. AETrack achieves 64.7 HOTA on MOT17 test set while running at 58 FPS and 62.8 HOTA on MOT20 at 52 FPS. Our code and models will be public soon.1

Slip parameter prediction is crucial for motion planning and control of unmanned skid-steering vehicles in off-road environments. Slip parameter prediction methods based on nonlinear observers and those based on machine learning models both have limitations under various conditions. Therefore, this paper presents a multi-layer Adaptive Unscented Kalman Filter (AUKF) slip parameter prediction method based on a fusion framework of nonlinear observers and machine learning models. The method first constructs the 0-layer AUKF using the vehicle kinematic model and sensor data to initialize the slip parameters. Then, with the input of the desired sequences of wheel speeds generated by the autonomous driving system, the 1-layer AUKF is constructed by combining the machine learning predictive model and running N times to obtain the future slip parameter sequence. Experimental data was collected by driving on paved and dirt roads with a skid-steering vehicle. The experimental results show that the method in this paper outperforms methods based on nonlinear observers in terms of slip parameter prediction accuracy when the prediction time domain is long. Furthermore, when faced with unknown conditions, this method shows superior robustness compared to methods based on machine learning models.

In the context of intelligent systems for human-vehicle collaboration, the fusion of information space, physical space, and user cognitive space has become a trend. This paper aims to address the challenges posed by information perception gaps and cognitive limitations experienced by drivers by leveraging Augmented Reality Head-up Displays (ie, AR-HUD) to compensate for perceptual deficiencies and enhance driver cognition. We introduce the innovative concept of the Human-Machine Team Situation Awareness (ie, TSA) loop model. Firstly, we analyze the cognitive characteristics of drivers and the spatiotemporal information elements within hazardous scenarios. Subsequently, AR-HUDs are employed to provide drivers with perceptual compensation and cognitive enhancement. Furthermore, we design AR-HUD interfaces for two representative scenarios. The results demonstrate that, with the support of AR-HUDs, the integration of dynamic interface elements proves to be more effective in compensating for perceptual deficiencies, and the inclusion of predictive information contributes to improved driving performance. Notably, in emergency situations, AR-HUDs play a crucial role in providing decision-enhancing information to drivers. The proposed theoretical framework offers opportunities for expanding the theoretical approaches and application domains of AR-HUDs.

As a cryptographic primitive supporting finer and richer access control, attribute-based encryption can provide more efficient, concise, secure and highly adaptive access control policies for cloud data, which has attracted the attention of many researchers. However, in a standard ABE scheme, the key can only be generated and issued by a central authority. The single authorization center has a heavy load and is vulnerable to attack. Once the center is paralyzed, it will bring serious security consequences. Multi-authorization center attribute encryption (MA-ABE) allows multiple parties to play an authoritative role, which can solve this problem. In this paper, we propose a MA-ABE scheme, which combines a global ID model, a two-stage sampling technique on a lattice and a monotonous linear secret sharing schemes (M-LSSS) to achieve a static security against arbitrary collusion in the random oracle model, in which the access policy of the scheme is expressed by DNF formula. Technically, our scheme is an improvement on the work of Datta et al. (2021), through this improvement, we can get shorter ciphertext and smaller key size.

With the popularity of cloud computing, a large number of personal and corporate data are outsourced to the cloud. This centralized storage and processing mode not only improves the efficiency of data processing, but also brings challenges to data security and privacy. In view of the development of quantum computing, the traditional public key encryption may no longer be secure in the future. Therefore, it is particularly important to study the public key authenticated keyword searchable encryption (PEAKS) scheme which can resist quantum. This scheme can provide double guarantee for the data in cloud storage: one is to ensure the confidentiality of the data, so that the original content cannot be decrypted even within the cloud service provider; the other is to allow users to perform keyword searches on encrypted data without decrypting the data. This provides a balance of security and efficiency for data search and analysis in cloud computing environment. Recently, Genise et al. designed a Gadget-based iNTRU trapdoor, which has the advantages of small size and high efficiency. Therefore, we design an efficient and secure public key authentication keyword searchable encryption scheme based on iNTRU lattice. The overall running time of this scheme is only more than 300ms.

Heterogeneous autonomous vehicle (HAV) coordinated motion planning must guide each vehicle out of the conflict zone based on the differences in vehicle platform characteristics. Decomposing complex driving tasks into primitives is an effective way to improve algorithm efficiency. Hence, the purpose of this paper is to complete the coordinated motion planning tasks through offline driving behavior primitive (DBP) library generation, online extension and selection of DBPs. The proposed algorithm applies dynamic movement primitives and singular value decomposition to learn driving behavior patterns from driving data, integrates them into a model-based optimization generation method as constraints, and builds a DBP library by fusing driving data and vehicle model. Based on the generated DBP library and primitive association probabilities learned from labeled driving segments via stochastic context-free grammar, the planning method completes the independent DBP extension of each vehicle in the conflict zone, generates an interaction DBP tree, and uses the mixed-integer linear programming algorithm to optimally select the primitives to be executed. This study demonstrates that the generated DBP library not only expands the types of primitives, but also distinguishes the characteristics of HAVs. We also present how to utilize the DBP libraries to obtain coordinated motion planning results with spatiotemporal information in the form of DBP extension and selection. The results obtained by real vehicle platforms and simulation show that the proposed method can accomplish coordinated motion planning tasks without relying on specific scene elements and highlight the unique motion characteristics of HAVs.

The path planning of unmanned differential steering vehicles (UDSVs) in the off-road environment not only needs to consider the non-complete constraints of vehicles but also faces the challenges of complex off-road terrains and obstacles. In this paper, an integrated path planning system is proposed to handle the influence of kinematic vehicle model, off-road terrains and obstacles systematically for the path planning of UDSVs. To improve the planning efficiency, a Pre-planning is designed and carried out using the Voronoi diagram established in the 3D environment with obstacles. By combining the potential field functions (PFF) related to passable obstacles and 3D terrains, an integrated PFF is defined to represent the movement cost of UDSV in the nonlinear optimal control (NOC) problem. Based on the NOC, a channel path planning (CPP) problem is formulated to avoid the untraceable path caused by the traditional line path planning (LPP). Simulation results show that the proposed system can plan a feasible path fast with the constraints from vehicle kinematics, obstacle avoidance and off-road terrains.

Developing an autonomous driving system which can generate human-like actions requires the ability to utilize the basic driving skills learned from the driving data. The efficiency of the algorithm can be significantly improved if we can decompose the complex driving tasks into motion primitives (MPs) which represent the elementary composition of driving skills. Therefore, the purpose of this paper is to represent MPs, extract MPs from unlabeled driving data, and then connect the learned MPs in the established library. By applying a probabilistic inference based on an Expectation-Maximization (EM) algorithm and initial segmentation, the extraction method segments the observed trajectories while learning a set of MPs represented by the modified dynamic movement primitives (DMPs). Moreover, the proposed connection algorithm transforms the connection problem into the re-representation problem of the MP sequence. This paper demonstrates that the modified DMP method can not only represent the driver's trajectory with acceptable accuracy but also have strong generalization ability. We also present how to utilize the mutual dependency between the representation and extraction to achieve MP segmentation and MP library establishment. Besides, this paper shows how the proposed connection algorithm correlates the independent MPs in the sequence to ensure a smooth transition and evaluates the tracking accuracy. The results show that the proposed method realizes the extraction of MPs and the re-generation of trajectory by making use of the interdependence relationship that is often neglected between the representation of a single MP, extraction of different types of MP and combination of multiple MPs.

How to integrate human factors into the motion planning system is of great significance for improving the acceptance of intelligent vehicles. Decomposing motion into primitives and then accurately and smoothly joining the motion primitives (MPs) is an essential issue in the motion planning system. Therefore, the purpose of this paper is to regenerate and join the learned MPs in the library. By applying a representation algorithm based on the modified dynamic movement primitives (DMPs) and singular value decomposition (SVD), our method separates the basic shape parameters and fine-tuning shape parameters from the same type of demonstration trajectories in the MP library. Moreover, we convert the MP joining problem into a re-presentation problem and use the characteristics of the proposed representation algorithm to achieve an accurate and smooth transition. This paper demonstrates that the proposed method can effectively reduce the number of shape adjustment parameters when the MPs are regenerated without affecting the accuracy of the representation. Besides, we also present the ability of the proposed method to smooth the velocity jump when the MPs are connected and evaluate its effect on the accuracy of tracking the set target points. The results show that the proposed method can not only improve the adjustment ability of a single MP in response to different motion planning requirements but also meet the basic requirements of MP joining in the generation of MP sequences.

This paper presents an Enhanced Adaptive Cruise Control (EACC) framework that can work in different modes according to the forward targets. The EACC system, which was proposed in this paper, is based on a unified model and can achieve speed tracking, stop & go and autonomous emergency braking (AEB). Notably, speed tracking does not require a real preceding vehicle, a virtual vehicle can be set in front of the EACC vehicle. The mathematical method of setting the virtual preceding vehicle and the switching logic between the different working modes of the EACC system were given. Employing a constraints softening method to avoid computing infeasibility, an optimal control law is numerically calculated using the CVXGEN solver. Finally, real vehicle tests show that the EACC framework provides significant benefits in terms of speed-tracking capability, safety and comfort requirements while satisfying driver desired car following characteristics for different driving situations.

Simultaneous localization and mapping (SLAM), as an important tool for vehicle positioning and mapping, plays an important role in the unmanned vehicle technology. This paper mainly presents a new solution to the LIDAR-based SLAM for unmanned vehicles in the off-road environment. Many methods have been proposed to solve the SLAM problems well. However, in complex environment, especially off-road environment, it is difficult to obtain stable positioning results due to the rough road and scene diversity. We propose a SLAM algorithm based on grid which combining probability and feature by Expectation-maximization (EM). The algorithm is mainly divided into three steps: data preprocessing, pose estimation, updating feature grid map. Our algorithm has strong robustness and real-time performance. We have tested our algorithm with our datasets of the multiple off-road scenes which obtained by LIDAR. Our algorithm performs pose estimation and feature map updating in parallel, which guarantees the real-time performance of the algorithm. The average processing time of each frame is about 55ms, and the average relative translation error is around 0.94%. Compared with several state-of-the-art algorithms, our algorithm has better performance in robustness and location accuracy.

Simultaneous Localization And Mapping (SLAM) plays a more and more important role in the environment perception system of Unmanned Ground Vehicle (UGV), most SLAM technologies used to be applied indoor or in urban scenarios, we present a real-time 6D SLAM approach suitable for large scale natural terrain with the help of an Inertial Measurement Unit(IMU) and two 3D Lidars. Besides dividing the entire map into many submaps which consists of large numbers of tree structure based voxels, we use probabilistic methods to represent the possibility of one voxel being occupied/null. A Sparse Pose Adjustment (SPA) method has been used to solve 6D global pose optimization with some relative poses as pose constraints and relative motions computed from IMU data as kinetics constraints. A place recognition method integrated a method named Rotation Histogram Matching (RHM) and a Branch and Bound Search (BBS) based Iterative Closest Points (ICP) algorithm is applied to realize a real-time loop closure detection. We complete global pose optimization with the help of Ceres. Experimental results obtained from a real large scale natural environment shows an effective reduction for Lidar odometry pose accumulative error and a good performance for 3D mapping.

Considering the driving habits which are learned from the naturalistic driving data in the path-tracking system can significantly improve the acceptance of intelligent vehicles. Therefore, the goal of this paper is to generate the prediction results of lateral commands with confidence regions according to the reference based on the learned motion primitives. We present a two-level structure for learning and generalizing motion primitives through demonstrations. The lower-level motion primitives are generated under the path segmentation and clustering layer in the upper-level. The Gaussian Mixture Model (GMM) is utilized to represent the primitives and Gaussian Mixture Regression (GMR) is selected to generalize the motion primitives. We show how the upper-level can help to improve the prediction accuracy and evaluate the influence of different time scales and the number of Gaussian components. The model is trained and validated by using the driving data collected from the Beijing Institute of Technology (BIT) intelligent vehicle platform. Experiment results show that the proposed method can extract the motion primitives from the driving data and predict the future lateral control commands with high accuracy.

The longitudinal control of an autonomous vehicle usually suffers from lateral interruptions, such as the cutting in/out of the lead vehicle, deteriorating its performance and even endangering driving safety. To address this problem, we present a model predictive-based approach for longitudinal control in autonomous driving by taking the lateral interruptions into account. First, a virtual lead vehicle scheme is introduced to predict the future behavior of the actual lead vehicle. By following the virtual lead vehicle rather than the actual lead vehicle, the control of the host vehicle is simplified to keep a proper following gap problem. Then, a strategic car-following gap (CFG) model, generated from highway naturalistic driving data, is employed to describe the safety hazard and the probability of cut-ins by other vehicles. A model predictive controller, incorporating the strategic CFG model as well as the acceleration and jerk limitations in the objective function, is designed for the longitudinal control of the host vehicle. Solving the optimal control problem can not only smooth the oscillation and overshoots caused by the lateral interruptions but also reduce the probability of cut-ins from the adjacent lanes. The proposed approach is simulated and validated through some predefined test scenarios in CarSim software.

A new technique for clutch self-calibration for automated mechanical transmission system based on single-axle parallel hybrid city bus is proposed. A vehicle equipped with this technique, will automatically calibrate the position of the half-engaged point of its clutch when it starts up. This calibration will eliminate or compensate the clearance due to wearing and temperature difference, and provide accurate parameters for transmission control unit, which transmission control unit can make the clutch acting rapidly and accurately, and improve the smoothness of launching and power performance, as well as shifting quality. Test results indicate this method is feasible and effective.

In this paper, we propose a novel, efficient stereo visual-odometry algorithm for ground vehicles moving in outdoor environments. To avoid the drawbacks of computationally-expensive outlier-removal steps based on random-sample schemes, we use a single-degree-of-freedom kinematic model of the vehicle to initialize an Iterative Closest Point (ICP) algorithm that is utilized to select high-quality inliers. The motion is then computed incrementally from the inliers using a standard linear 3D-to-2D pose-estimation method without any additional batch optimization. The performance of the approach is evaluated against state-of-the-art methods on both synthetic data and publicly-available datasets (e.g., KITTI and Devon Island) collected over several kilometers in both urban environments and challenging off-road terrains. Experiments show that the our algorithm outperforms state-of-the-art approaches in accuracy, runtime, and ease of implementation.

Complex robotic systems often have to operate in large environments. At the same time, their dynamic is complex enough that path planning algorithms need to reason about the kinodynamic constraints of these systems. On the other hand, such robotic systems are typically expected to operate with speed that is commensurate with that of humans. This poses stringent limitation on available planning time. These will result in a contradiction between planning efficiency and the dimensions of the state space determined by the kinodynamic constraints. In this paper we present an anytime path planning algorithm to solving this problem. First, a graduated state space which consists of state lattices and grids is constructed for planning. Then, ARA* algorithm is utilized to search the graduated state space to find a path that satisfies the kinodynamic constraints and available runtime of planning.

This paper presents a novel method to realize on-board visual odometry system. Vehicular kinematic constrain is used in the motion estimation algorithms. The work is a extension from planar steering model to 3-dof in which vehicle's motion is modeled more reasonable and accurate. By virtue of appropriate simplification, the close-form solution of motion parameters can be obtained only need to find real roots of a cubic equation. Then optimization based refine method can bring the winner solution to accurate solution utilizing inliers founded. The algorithm has been tested on both simulation platform and real car test and achieved promising results.

The 2011 Intelligent Vehicle Future Challenge (11'IVFC) tested self-driving systems in real urban scenarios. The entry of Beijing Institute of Technology: BIT-III finished the 10-kilometer long track in 28 minutes without human operation and obeyed traffic regulations in most circumstances. This paper presented the design and implementation of BIT-III. As a universal self-driving system, BIT-III valued extensibility and featured modularized system architecture. For a better compatibility with diverse sensing devices, BIT-III classified perception to be either OGM (Occupancy Grid Map)-oriented or object-oriented based on the output mode. To work in environments with uncertainties, BIT-III gave first priority to safety and stability in driving, and realized them in the core-level components as the instinct of the system. Even in the unknown environment in the ll'IVFC, BIT-III was able to drive smoothly without crashes.

We present an iterative linear quadratic regulator (ILQR) method for trajectory tracking control of a wheeled mobile robot system. The proposed scheme involves a kinematic model linearization technique, a global trajectory generation algorithm, and trajectory tracking controller design. A lattice planner, which searches over a 3D ( x, y, θ ) configuration space, is adopted to generate the global trajectory. The ILQR method is used to design a local trajectory tracking controller. The effectiveness of the proposed method is demonstrated in simulation and experiment with a significantly asymmetric differential drive robot. The performance of the local controller is analyzed and compared with that of the existing linear quadratic regulator (LQR) method. According to the experiments, the new controller improves the control sequences ( ν, ω ) iteratively and produces slightly better results. Specifically, two trajectories, ‘S’ and ‘8’ courses, are followed with sufficient accuracy using the proposed controller.

This paper presents an approach to detect and recognize traffic signs present in the urban scenes in China. The algorithm is composed of three steps that are color segmentation, shape detection and pictogram recognition. In the first step Ridge Regression is used to obtain a precise segmentation in RGB color space and achieves the same good performance as many machine learning based methods while using less computation time. Recognition process include a novel feature extraction involves OTSU method, and the feature extracted is robust against illumination variations and distortions. The algorithm has been run on several thousands of images with promising results.

Rank and census transforms provide high resistance to radiometric distortion, vignette, and noise because they are based on the relative ordering of local pixel intensity values rather than the pixel values themselves. These transforms are widely used in many computer vision applications. An important step of computing these transforms is to compare or rank two grayscale values, which is very much like measuring color difference in color image. Color difference between two color points at any part of a uniform color space corresponds to the perceptual difference between the two colors by the human vision system. Based on this idea, we propose to use perceptual color contrast to implement color rank and census transforms and achieve this without significantly increasing the amount of data to process and without complicated computations. Furthermore, we demonstrate the feasibility of using these new transforms to find correspondences for stereo vision.

The recognition and tracking of traffic lights for intelligent vehicles based on a vehicle-mounted camera are studied in this paper. The candidate region of the traffic light is extracted using the threshold segmentation method and the morphological operation. Then, the recognition algorithm of the traffic light based on machine learning is employed. To avoid false negatives and tracking loss, the target tracking algorithm CAMSHIFT (Continuously Adaptive Mean Shift), which uses the color histogram as the target model, is adopted. In addition to traffic signal pre-processing and the recognition method of learning, the initialization problem of the search window of CAMSHIFT algorithm is resolved. Moreover, the window setting method is used to shorten the processing time of the global HSV color space conversion. The real vehicle experiments validate the performance of the presented approach.

The first Chinese unmanned ground vehicles competition - The 2009 Future Challenge: Intelligent Vehicles and Beyond (FC'09) pushed China's unmanned vehicles out of laboratories and into application environments. In order to further promote the development of unmanned vehicle technologies, the test and evaluation system for unmanned vehicles needs to be studied. The design method of test environment is proposed in accordance with the definition and classification of test environment elements. Based on the multi-platform and multi-sensor, an omnidirectional video monitoring test system of unmanned vehicles is built. The fuzzy comprehensive evaluation method combined with AHP (analytic hierarchy process) is applied to the comprehensive evaluation of unmanned vehicles. The evaluation examples of unmanned vehicles show that the proposed evaluation system can quantitatively evaluate the overall technical performance and individual technical performance of unmanned vehicles.

The 2009 Future Challenge - Intelligent Vehicle and Beyond (FC'09) was held in Xi'an, China. Our intelligent vehicle named BIT participated in all competitions at this event. This paper describes BIT's system structure and its capabilities. BIT combines a global path planning method and local path planning to drive the vehicle to address the challenges posted by the unknown competition environment. A novel curve tracking strategy based on preview and curve bisector is developed for complex paths such as U-turn. For recognizing traffic lights, Haar feature and AdaBoost algorithm are used to train and obtain traffic light classifiers. Normalization of every candidate region in RGB and HSV spaces is performed and compared with a threshold to fulfill the verification. The experiment describes BIT's performance and the conclusion sets forth the main work in the next step.

Road detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view road detection. Specifically, we propose using Support Vector Machines (SVM) for road detection and effective approach for self-supervised online learning. The proposed road detection algorithm is capable of automatically updating the training data for online training which reduces the possibility of misclassifying road and non-road classes and improves the adaptability of the road detection algorithm. The algorithm presented here can also be seen as a novel framework for self-supervised online learning in the application of classification-based road detection algorithm on intelligent vehicle.

Many people die each year in the world in single vehicle roadway departure crashes caused by driver inattention, especially on the freeway. Lane Departure Warning System (LDWS) is a useful system to avoid those accident, in which, the lane detection is a key issue. In this paper, after a brief overview of existing methods, we present a robust lane detection algorithm based on geometrical model and Gabor filter. This algorithm is based on two assumptions: the road in front of vehicle is approximately planar and marked which are often correct on the highway and freeway where most lane departure accidents happen. The lane geometrical model we build in this paper contains four parameters which are starting position, lane original orientation, lane width and lane curvature. The algorithm is composed of three stages: the first stage is called off-line calibration which just runs once after the camera is mounted and fixed in the vehicle. The parameters of camera used for lane detection is accurately estimated by the 2D calibration method; The second stage is called lane model parameters estimation and lane model candidates construction, the first three parameters, starting position, lane original orientation and lane width will be estimated using dominant orientation estimation and local Hough transform. Then the construction of lane model candidates is implemented for the final lane model matching; the third stage is model matching. The proposed lane module matching algorithm is implemented to match the best fitted lane model. The combination of these modules can overcome the universal lane detection problems due to inaccuracies in edge detection such as shadow of tree and passengers on the road. Experimental results on real road will be presented to prove the effectiveness of the proposed lane detection algorithm.