Identifying hazardous traffic conditions related to traffic collisions is important to the development of real-time traffic control measures for preventing collision occurrences. The primary objective of this study is to explore the hazardous traffic situations on freeways for rear-end and sideswipe collisions to assist the development of control strategies for mitigating collision risks using the conditional inference tree method. Based on the 3-year crash data and traffic data from a freeway corridor on the Interstate 880 in California, the conditional inference tree was developed and validated for each collision type separately. Results showed that the hazardous traffic conditions were different between different types of collisions. The occurrence of rear-end collision was mainly related to the magnitude of lengthwise traffic variation between upstream and downstream locations. The occurrence of sideswipe collision was related to the type of freeway segment as well as the crosswise traffic variation between adjacent lanes. The control strategies for the mitigation of collision potentials were discussed according to the appearances of the conditional inference trees developed in this study.

The deployment of new emerging technologies, such as cooperative systems, allows the traffic community to foresee relevant improvements in terms of traffic safety and efficiency. Autonomous vehicles are able to share information about the local traffic state in real time, which could result in a better reaction to the mechanism of traffic jam formation. An upstream single-hop radio broadcast network can improve the perception of each cooperative driver within a specific radio range and hence the traffic stability. The impact of vehicle to vehicle cooperation on the onset of traffic congestion is investigated analytically and through simulation. A next generation simulation field dataset is used to calibrate the full velocity difference car-following model, and the MOBIL lane-changing model is implemented. The robustness of the calibration as well as the heterogeneity of the drivers is discussed. Assuming that congestion can be triggered either by the heterogeneity of drivers’ behaviours or abnormal lane-changing behaviours, the calibrated car-following model is used to assess the impact of a microscopic cooperative law on egoistic lane-changing behaviours. The cooperative law can help reduce and delay traffic congestion and can have a positive effect on safety indicators.

A real-time scene modelling approach is presented that recognises temporary and permanent road structure change resulting from construction, accident or lane expansion and other obstructions. The system defined utilises a two-phase approach to modelling the scene. In the transitional phase, a dominant set-based graphical clustering approach is applied to understand the current scene structure from trajectory groupings, whereas the operational phase analyses the trajectories in real-time to detect anomalies such as u-turns, wrong-way or erratic drivers based on the acquired model of the scene structure and normal traffic patterns. In addition, the concept of dynamic traffic flow analysis is utilised to identify and remember temporary additions and removals of paths due to construction and accidents, as well as permanent road structure changes. An intuitive equal-arc-length sampling is applied to extract only the spatial information from the trajectory comparisons, since the spatial characteristics alone are sufficient for road structure understanding. A distance metric is developed to measure spatial difference and directional change of the path with entrance and exit awareness. Results for a publicly available dataset are provided, demonstrating that the method can efficiently model the scene, detect anomalies and capture both temporary and permanent scene reconstructions.

Within this study the outcomes of two driving simulator studies are reported. In the first study, three different human–machine-interaction modalities for an ecological driving support system were compared concerning driving behaviour, workload and user acceptance parameters in order to find out whether a haptic acceleration/deceleration and gear shifting advice improved the ecological driving style additionally to a visual feedback system. Results showed that the combination of the visual and haptic modality led to the fastest reaction times and smallest deviations from the optimal acceleration and gear-shift behaviour. Yet, the results of the acceptance questionnaires revealed that the participants preferred the visual display, whereas the haptic feedback showed the best results regarding the workload scores. The second study dealt with the timing of the feedback. When should ecological coasting advices be presented in order to reach a high-user acceptance? As an outcome, the participants decelerated earlier receiving a coasting advice. However, if the advice was given too early the acceptance of the participants was low.

Pothole avoidance may be considered similar to other obstacle avoidance, except that the potholes are depressions rather than extrusions from a surface. This study discusses a solution for detection of potholes in the path of an autonomous vehicle operating in an unstructured environment. Here, a vision approach is used since the simulated potholes are significantly different from the background surface. Furthermore, using this approach, pothole can only be detected in case of uniform lighting conditions. The solution to the problem is developed in a systematic manner. Initially, a specific camera and frame grabber are chosen, then camera is mounted on top of the autonomous vehicle and the images will be acquired. Then, a software solution is designed using MATLAB. The method is tested under real-time conditions and results demonstrate its reasonable efficiency.

The study proposes an active collision avoidance system to allow safe lane-changing manoeuvres by self-steering vehicles in the presence of the uncertainties associated with nearby vehicles and the surrounding environment. This system integrates estimation of conflict probability, model predictive control and dedicated short-range communications (DSRC) techniques to ensure a collision-free operation. To accomplish this, the proposed system uses model predictive control to predict the future positions of vehicles and estimates the conflict probability so as to reduce the risk of collision. The system also exploits DSRC techniques to facilitate the gathering of information from nearby vehicles so that potential conflicts can be detected at an earlier stage. Autonomous vehicles can thus make adjustments based on the acquired data to avoid collisions in a real communication environment. The effectiveness of the method has been verified under experimental conditions. The influences of key parameters in the control method are examined.

A runway incursion prevention system for high density airports based on an event-driven sensor network is presented, in which non-cooperative active detection sensors, such as microwave or infrared sensors installed beside the critical points of runways and taxiways other than SMR, ADS-B, or MLAT to detect and locate aircraft or vehicle continuously, and the discrete airdrome surface traffic situation can be reconstructed based on the fusion of object sensing events detected by a sensor network and the landing and taking off events reported by pilots. Thus, an airdrome surface operation model based on Petri Nets is proposed based on runway operation rules in the present study. After defining the runway prevention control regulations, runway incursion detection and prevention was transformed into a state forbidden problem. A logical controller that satisfied a strong regulatory condition design method was developed for uncontrollable events in the airdrome surface operation model. The logical controller we proposed is centralised and maximally permissive, and the runway incursion prevention control actions, such as airfield lighting system, can be adopted to execute the discrete control policies generated by logical controller automatically. The illustrated case demonstrated that a controller designed with stop-bar lights or center-line guidance lights could prevent runway incursions in an efficient manner, while its time complexity depended only on the sensor network configurations rather than the number of objects, which is more suitable for high density airports. In general, the combination of active event-driven sensor and discrete event controller is a RIPS solution.

In the authors previous study, the authors proposed deceleration for collision avoidance (DCA) as an index to evaluate collision risks against forward obstacles and examined the effectiveness of their forward obstacles collision warning system (FOCWS) based on DCA. In the present manuscript, they improve the visual interface of the FOCWS, and conduct driving simulator experiments to quantitatively evaluate the effectiveness of the improved FOCWS in situations where a preceding vehicle decelerates abruptly. The experimental results revealed that the FOCWS based on DCA was effective in assisting drivers to shorten the reaction time and to avoid collisions. Moreover, in the subjective assessment questionnaire, a significant number of experimental participants reported that the FOCWS based on DCA could evaluate collision risks more properly compared with the FOCWS based on a time-to-collision.